Predictive Parcel Damage Identification, Analysis, And Mitigation

ABSTRACT

A first parcel digital image associated with a first interaction point is received. The first parcel digital image may be associated with a first parcel being transported to or from the first interaction point. At least a second parcel digital image associated with at least a second interaction point is further be received. The second parcel digital image may be associated with the first parcel being transported to or from the second interaction point. A first parcel damage analysis is automatically generated based at least in part on analyzing the first parcel digital image and the at least second parcel image. The damage analysis can include determining whether the first parcel is damaged above or below a threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/032,958, filed Sep. 25, 2020, which claims priority to U.S. patentapplication Ser. No. 16/148,104 filed Oct. 1, 2018, which claimspriority to U.S. Patent Application No. 62/565,404 filed Sep. 29, 2017,entitled SYSTEMS AND METHODS FOR PREDICTIVE PARCEL DAMAGEIDENTIFICATION, ANALYSIS, AND MITIGATION, each application of which ishereby incorporated by reference in its entirety.

FIELD

Aspects of the present disclosure relate to the capture of digitalimages of parcels; the detection, characterization, diagnosis, costanalysis, and root cause analysis of any damage based upon machinelearning; and the automatic mitigation of the root cause of damage.

BACKGROUND

Parcels (e.g., packages, containers, letters, items, pallets, etc.) aretransported from an origin to a destination and may have variousintermediate locations (e.g., sorting facilities) and interactionsduring such transport. Naturally, an increase in the number of locationsand interactions during transport increases the number of possibledamaging situations for the parcels. If a package is damaged during thetransport process, a shipping and logistics provider may be responsiblefor the damages. However, it may be difficult to determine if the parcelwas damaged at the time it was picked up or where the parcel may havebeen damaged during transport. Further, if a particular point of damageis located, it may be difficult to mitigate such damaging conditions inan efficient manner.

Existing technologies for identifying and/or assessing damaged parcelsmay include software applications that are passively configured toreceive manual input from users indicating damage has occurred toparticular parcels. Accordingly, these applications only identify thedamage based on user input. These applications and other technologies(e.g., Internet of Things (IoT) devices) have shortcomings by failing toprovide: automated detection of the damage, diagnosis or classificationof the damage, cost analysis of the damage, machine learning associatedwith the damage, modifications of conditions or devices, and otherfunctionalities. Various embodiments of the present disclosure improvethese existing technologies by overcoming some or each of theseshortcomings, as described in more detail herein.

SUMMARY

Various embodiments of the present disclosure are directed to anapparatus, a computer-implemented method, and a system. In someembodiments, the apparatus is used for predictive parcel damagemitigation in a parcel transit network. The parcel transit network mayinclude an origin interaction point, a plurality of parcel interactionpoints (e.g., air gateways and consolidation hubs), and a destinationinteraction point. The apparatus can include at least one processor andat least one memory including computer program code. The at least onememory and the computer program code can be configured to, with the atleast one processor, cause the apparatus to perform the followingoperations according to certain embodiments. A first plurality of parceldigital images is received from the origin interaction point. The firstplurality of parcel digital images is associated with a parcel beingtransported from the origin interaction point to the destinationinteraction point via the plurality of parcel interaction points. Asecond plurality of parcel digital images of the parcel is received froma first parcel interaction point of the plurality of parcel interactionpoints. The first plurality of parcel digital images and the secondplurality of parcel digital images may represent a plurality of fieldsof view of the parcel. A first parcel damage analysis isprogrammatically generated based upon the first plurality of parceldigital images, the second plurality of parcel digital images, and amachine learning model.

In some embodiments, the computer-implemented method includes thefollowing operations. A first parcel digital image associated with afirst interaction point is received. The first parcel digital image maybe associated with a first parcel being transported to or from the firstinteraction point. At least a second parcel digital image associatedwith at least a second interaction point is further be received. Thesecond parcel digital image may be associated with the first parcelbeing transported to or from the second interaction point. A firstparcel damage analysis is automatically generated based at least in parton analyzing the first parcel digital image and the at least secondparcel image. The damage analysis can include determining whether thefirst parcel is damaged above or below a threshold.

In some embodiments, the system includes at least one first computingdevice having at least one processor and at least one computer readablestorage medium having program instructions embodied therewith. In someembodiments, the program instructions are readable or executable by theat least one processor to cause the system to perform the followingoperations. At least a first parcel digital image captured from one ormore physical locations within a parcel transit network is received. Thefirst parcel digital image includes a representation of a first parcel.The parcel transit network may correspond to a plurality of physicallocations traversed by the first parcel along one or more carrierroutes. In response to analyzing the at least first parcel digitalimage, a likelihood associated with a damage of the first parcel isdetermined. Based at least on the determining of the likelihoodassociated with the damage, a signal is provided to a second computingdevice. The providing causes the computing device to be modified or acondition to be modified.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the disclosure in general terms, reference willnow be made to the accompanying drawings, which are not necessarilydrawn to scale, and wherein:

FIG. 1 provides an illustration of an exemplary embodiment of thepresent

disclosure;

FIG. 2 provides a schematic of an analysis computing entity according toone embodiment of the present disclosure;

FIG. 3 provides an illustrative schematic representative of a mobilecomputing entity 110 that can be used in conjunction with embodiments ofthe present disclosure;

FIG. 4 illustrates an example autonomous vehicle that may be utilized invarious embodiments;

FIG. 5 illustrates an example manual delivery vehicle according tovarious embodiments;

FIGS. 6A and 6B includes an illustration of a conveying mechanismaccording to one embodiment of the present disclosure and an exemplarymulti-view image capture system for use with embodiments of the presentdisclosure;

FIG. 7 illustrates an exemplary parcel transit route for use withembodiments of the present disclosure;

FIG. 8 illustrates an exemplary process for use with embodiments of thepresent disclosure; and

FIG. 9 illustrates an exemplary process for use with embodiments of thepresent disclosure.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the disclosure are shown. Indeed, the disclosure may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein. Rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

I. Computer Program Products, Methods, and Computing Entities

Embodiments of the present disclosure may be implemented in variousways, including as computer program products that comprise articles ofmanufacture. A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, program code, and/or similar terms usedherein interchangeably). Such non-transitory computer-readable storagemedia include all computer-readable media (including volatile andnon-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM)), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), doubleinformation/data rate synchronous dynamic random access memory (DDRSDRAM), double information/data rate type two synchronous dynamic randomaccess memory (DDR2 SDRAM), double information/data rate type threesynchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamicrandom access memory (RDRAM), Twin Transistor RAM (TTRAM), Thyristor RAM(T-RAM), Zero-capacitor (Z-RAM), Rambus in-line memory module (RIMM),dual in-line memory module (DIMM), single in-line memory module (SIMM),video random access memory (VRAM), cache memory (including variouslevels), flash memory, register memory, and/or the like. It will beappreciated that where embodiments are described to use acomputer-readable storage medium, other types of computer-readablestorage media may be substituted for or used in addition to thecomputer-readable storage media described above.

As should be appreciated, various embodiments of the present disclosuremay also be implemented as methods, apparatus, systems, computingdevices/entities, computing entities, and/or the like. As such,embodiments of the present disclosure may take the form of an apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. However, embodiments of the presentdisclosure may also take the form of an entirely hardware embodimentperforming certain steps or operations.

Embodiments of the present disclosure are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computingdevices/entities, computing entities, and/or the like carrying outinstructions, operations, steps, and similar words used interchangeably(e.g., the executable instructions, instructions for execution, programcode, and/or the like) on a computer-readable storage medium forexecution. For example, retrieval, loading, and execution of code may beperformed sequentially such that one instruction is retrieved, loaded,and executed at a time. In some exemplary embodiments, retrieval,loading, and/or execution may be performed in parallel such thatmultiple instructions are retrieved, loaded, and/or executed together.Thus, such embodiments can produce specifically-configured machinesperforming the steps or operations specified in the block diagrams andflowchart illustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

II. Exemplary Definitions

As used herein, the terms “data,” “content,” “digital content,” “digitalcontent object,” “information,” and similar terms may be usedinterchangeably to refer to data capable of being transmitted, received,and/or stored in accordance with embodiments of the present disclosure.Thus, use of any such terms should not be taken to limit the spirit andscope of embodiments of the present disclosure. Further, where acomputing device is described herein to receive data from anothercomputing device, it will be appreciated that the data may be receiveddirectly from another computing device or may be received indirectly viaone or more intermediary computing devices/entities, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like, sometimes referred to herein as a“network.” Similarly, where a computing device is described herein totransmit data to another computing device, it will be appreciated thatthe data may be sent directly to another computing device or may be sentindirectly via one or more intermediary computing devices/entities, suchas, for example, one or more servers, relays, routers, network accesspoints, base stations, hosts, and/or the like.

The term “parcel damage mitigation” refers to measures that entitiestraversing and/or overseeing a parcel transit network may employ tomitigate damage caused to parcels in transit while traversing the parceltransit network. Examples of parcel damage mitigation may includeadjustment of temperature (or other environmental parameters) at alocation within the parcel transit network, decommissioning (temporaryor otherwise) of a conveyor belt or other vehicle within the parceltransit network, adjusting the speed of a conveyor belt or other vehiclewithin the parcel transit network, and the like.

The terms “parcel transit network,” “carrier's logistic network,” or“transportation and logistics network” refer to a series of one or morephysical locations traversed by a parcel, carrier, and/or carrierapparatus (e.g., vehicle, drone, etc.) between an origin location (e.g.,drop-off location for a package) and a destination location (e.g., anintermediate sorting facility and/or a destination address). Forexample, a parcel transit network can be or include some or each aspectof the parcel transit route 700 of FIG. 7 .

The term “origin interaction point” refers to a physical location withina parcel transit network or carrier's logistic network where aparticular parcel is first encountered. Examples of origin interactionpoints include a residence, a transit network drop box, and a place ofbusiness.

The term “parcel interaction point” refers to a physical location withina parcel transit network or carrier's logistic network where anyinteraction with a particular parcel may occur. Interaction may bedefined as any physical contact (e.g., the picking up of a parcel),including transfer from one location and/or vehicle to another. Examplesof physical locations and vehicles within the parcel transit network areoutlined herein and are apparent to those skilled in the art. Asdescribed herein, one or more digital image capturing mechanisms/devicescan be located at parcel interaction points and/or anywhere betweenparcel interaction points within the parcel transit network.

The term “destination interaction point” refers to a physical locationwithin a parcel transit network where a particular parcel is intended tobe delivered. As such, the destination interaction point, in someembodiments, is the final intended parcel interaction point along thetraversal of the parcel transit network for the particular parcel.Alternatively or additionally, in some embodiments, the destinationinteraction point is an intermediate point along traversal of the parceltransit network, such as an intermediate facility (e.g., an air gatewayor consolidation hub).

The term “parcel digital image” refers to a digitally captured image(e.g., a digital photo) and/or set of images (e.g., a video sequence)representing one or more aspects of a particular parcel within a parceltransit network. In some embodiments, a parcel digital image of aparticular parcel is captured using a digital camera. In otherembodiments, a parcel digital image is captured using other means ofcapturing digital representations or the like of a particular parcel.

The terms “parcel, “item,” and/or “shipment” refer to any tangibleand/or physical object, such as a package, a container, a load, a crate,items banded together, an envelope, suitcases, vehicle parts, pallets,drums, vehicles, and the like sent through a delivery service from afirst geographical location to one or more other geographical locations.

The terms “field of view,” “fields of view,” and “pose range” refer to arestriction to what is visible and/or available to be captured by adigital image capturing apparatus (e.g., camera) or device.

The term “parcel damage analysis” refers to an analysis of damage causedto a parcel (e.g., external or internal) by any of a plurality ofexternal factors (e.g., related to a parcel transit network or otherfactor). For instance, damage analysis may include the quantity ofparcels damages, the type of damage, and/or the severity of damagecaused to one or more parcels.

The term “threshold” refers to a limit associated with a level of parceldamage that is deemed acceptably by a transit network provider. Forexample, a transit network provider may deem it acceptable for a parcelto have minimal water damage that smudges lettering as part of anintended recipient's address on an exterior of the parcel. Such minimaldamage may be associated with a numerical value and/or category that maybe compared with the threshold. In another example, the transit networkprovider may deem it unacceptable (e.g., outside, below, or above thethreshold) for a parcel to have a shredded or otherwise compromisedcorner. Such unacceptable damage may be associated with a numericalvalue and/or category that may be compared with the threshold.

The term “transit network interaction point condition confirmation”refers to a digital representation of a positive, safe, and/orauthorized condition of a transit network interaction point. Forexample, a transit network interaction point condition confirmation maycomprise an indication that all conditions at an interaction point aresafe for the transit of a parcel to remain or continue traversing atransmit network, which indicates damage has not been detected above orbelow a threshold.

The term “transit network interaction point damage analysis” refers to aparcel damage analysis that is associated with a point within a transitnetwork. In embodiments, the point within the transit network is a knownor predetermined interaction point for a particular parcel. Inembodiments, a parcel may have passed through (i.e., interacted with) atransit network point without having been damaged. In such anembodiment, a transit network interaction point damage analysis mayinclude a notification reflecting such successful traversal.

It should be appreciated that the term “programmatically expected”indicates machine prediction of occurrence of certain events.

As used herein, the term “likelihood” refers to a measure of probabilityfor occurrence of a particular event. For example, in some embodiments,an output layer of a machine learning model may output a floating pointvalue score or probability that an input image is of a particularclassification (e.g., a damaged parcel).

The term “machine learning model” refers to a model that is used formachine learning tasks or operations. A machine learning model cancomprise a title and encompass one or more input images or data, targetvariables, layers, classifiers, etc. In various embodiments, a machinelearning model can receive an input (e.g., an image taken at aninteraction point), and based on the input identify patterns orassociations in order to predict a given output (e.g., classify theimage as either a damaged or non-damaged parcel). Machine learningmodels can be or include any suitable model, such as one or more: neuralnetworks, word2Vec models, Bayesian networks, Random Forests, BoostedTrees, etc. “Machine learning” as described herein, in particularembodiments, corresponds to algorithms that parse or extract features ofhistorical data (e.g., a data store of historical images), learn (e.g.,via training) about the historical data by making observations oridentifying patterns in data, and then receive a subsequent input (e.g.,a current image) in order to make a determination, prediction, and/orclassification of the subsequent input based on the learning withoutrelying on rules-based programming (e.g., conditional statement rules).

The term “target variable” refers to a value or classification that amachine learning model is designed to predict. In some embodiments,historical data is used to train a machine learning model to predict thetarget variable (e.g., whether damage is classified as “water damage,”“heat damage,” “compression damage,” “tear damage,” etc.). Historicalobservations of the target variable are used for such training.

The term “machine learning model experiment” refers to a method forpredicting the target variables that comprise a machine learning model.The machine learning model experiment represents a certain set offeatures provided to a certain algorithm with a certain set ofhyper-parameters. A machine learning model experiment can haveassociated therewith a machine learning model experiment name and amachine learning model experiment description.

The term “machine learning model selection” refers to an electronicselection of a machine learning model available for inclusion in amachine learning model experiment. A machine learning model selectioncan be one or more of a touch screen input, mouse click or keyboardentry input provided to a computing device, and the machine learningmodel selection can be made from a displayed menu of several availablemachine learning models.

The terms “dataset” and “data set” refer to a collection of data. A dataset can correspond to the contents of a single database table, or asingle statistical data matrix, where every column of the tablerepresents a particular variable, and each row corresponds to a givenmember of the data set in question. The data set can be comprised oftuples.

The term “transit network interaction point damage mitigationinstruction” refers to a set of digital instructions providing signalsto any of one or more parcel interaction points (or devices within suchpoints) within a transit parcel network or instructions to other devices(e.g., notifications to any computing device at any location indicatingsteps to take to mitigate the damage) regarding modification of any ofone or more environmental or structural conditions. In some embodiments,the digital instruction includes an actual control signal that directlymodified a condition to mitigate or stop the damage as described herein.In some embodiments, the digital instruction is a notification to a userdevice specifying what steps a user must take to modify or mitigatedamage. In embodiments, such digital instructions are based upon adetermination that one or more parcels have been damaged in a particularway by traversing through the parcel interaction point(s) and that thedigital instructions may lead to fewer damaged parcels or theelimination of damage to parcels traversing through the parcelinteraction point(s).

The term “parcel view overlap” refers to any overlap or duplication of aportion of digital images representing a parcel. For example, a sideview of a parcel and a frontal view of a parcel, while technicallyrepresenting two fields of view, may have overlapping segments of theparcel.

The term “transit network interaction point identifier” refers to adigital identifier associated with a physical interaction point (e.g.,geo-coordinates) within a transit network.

The term “parcel identifier” refers to a digital identifier associatedwith a parcel that is traversing a transit network. Accordingly, aparcel identifier can identify a particular parcel.

The term “parcel damage analysis summary” refers to one or more items ofdata, such as digital data included in a data structure, and which isassociated with an analysis of damage associated with a parceltraversing a transit network. For example, after damage is associatedwith a parcel, the parcel damage analysis summary can include a parceltype of the damaged parcel, a damage type associated with the parcel, aparcel damage location identifier associated with the damaged parcel, aparcel damage severity associated with the parcel, a parcel damagemitigation recommendation associated with the parcel, and a parceldamage restoration estimate associated with the parcel.

The term “parcel type” refers to a digital representation of aclassification or categorization of a parcel. For example, a parcel maybe classified as an envelope, a small box, a large box, a vehicle, andthe like. In various embodiments, some or each of the parcel type is anoutput (e.g., a fully connected layer output in a neural network) forclassifying the parcel type in one or more machine learning models.

The term “parcel damage type” refers to a digital representation of aclassification of a type of damage caused to a parcel. For example,damage to a parcel may be classified as water damage, extremetemperature exposure, constitutional (exterior or interior) damageresulting from unsustainable squeezing or other crushing of the parcel,belt burn (i.e., damage resulting from a conveyor belt as describedherein), drop induced damage (i.e., the parcel was dropped on the flooror flooring), shredding, and the like. In various embodiments, some oreach of the parcel damage types are an output for classifying the damagetype in one or more machine learning models.

The term “parcel damage location identifier” refers to a digitalidentifier associated with a location (e.g., geo-coordinates) within atransit network that is known to be associated with damage to aparticular parcel. For example, any location where the parcel damagebegan or first identified can correspond to the parcel damage locationidentifier. Alternatively or additionally, any location where the parcelcontinues to be damaged or incurs more damage can correspond to theparcel damage location identifier.

The term “parcel damage severity” refers to a characterization of alevel of severity associated with damage caused to a parcel. The parceldamage severity can include cardinality level categorizations, such as“not severe,” “moderately sever,” and/or severe, and/or includecontinuous non-categorical level severity, such as integers that aredirectly proportional to the severity (e.g., on a scale of 1 through 10,1 is not damaged at all and 10 is the most damaged a parcel can get). Insome embodiments, parcel damage severity is based on pixel variationsbetween images as analyzed by one or more machine learning models, asdescribed in more detail below.

The term “parcel damage mitigation recommendation” refers to one or morepotential mitigation techniques that, if employed, may prevent or helpprevent a particular type of parcel damage known to be caused at aparticular parcel interaction point within a parcel transit network.

The term “parcel damage restoration estimate” refers to a digitalrepresentation of a monetary, time-based, or other factor estimateassociated with restoring or replacing known damaged parcels. Forexample, the parcel damage restoration estimate can include a cost, interms of time and/or money that a specific damage to a parcel will taketo restore the damaged parcel back to a non-damaged state.

III. Exemplary System Architecture

FIG. 1 provides an illustration of an exemplary embodiment of thepresent disclosure. As shown in FIG. 1 , this particular embodiment mayinclude one or more manual delivery vehicles 100, one or more analysiscomputing entities 105, one or more mobile computing entities 110, oneor more satellites 112, one or more autonomous vehicles 140, one or morenetworks 135, and/or the like. Each of these components, entities,devices, systems, and similar words used herein interchangeably may bein direct or indirect communication with, for example, one another overthe same or different wired or wireless networks. Additionally, whileFIG. 1 illustrates the various system entities as separate, standaloneentities, the various embodiments are not limited to this particulararchitecture.

1. Exemplary Analysis Computing Entities

FIG. 2 provides a schematic of an analysis computing entity 105according to particular embodiments of the present disclosure. Ingeneral, the terms computing entity, computer, entity, device, system,and/or similar words used herein interchangeably may refer to, forexample, one or more computers, computing entities, desktops, mobilephones, tablets, phablets, notebooks, laptops, distributed systems,consoles input terminals, servers or server networks, blades, gateways,switches, processing devices, processing entities, set-top boxes,relays, routers, network access points, base stations, the like, and/orany combination of devices or entities adapted to perform the functions,operations, and/or processes described herein. Such functions,operations, and/or processes may include, for example, transmitting,receiving, operating on, processing, displaying, storing, determining,creating/generating, monitoring, evaluating, comparing, and/or similarterms used herein interchangeably. In particular embodiments, thesefunctions, operations, and/or processes can be performed on data,content, information/data, and/or similar terms used hereininterchangeably.

As indicated, in particular embodiments, the analysis computing entity105 may also include one or more communications interfaces 220 forcommunicating with various computing entities, such as by communicatingdata, content, information/data, and/or similar terms used hereininterchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like.

As shown in FIG. 2 , in particular embodiments, the analysis computingentity 105 may include or be in communication with one or moreprocessing elements 205 (also referred to as processors, processingcircuitry, and/or similar terms used herein interchangeably) thatcommunicate with other elements within the analysis computing entity 105via a bus, for example. As will be understood, the processing element205 may be embodied in a number of different ways. For example, theprocessing element 205 may be embodied as one or more complexprogrammable logic devices (CPLDs), microprocessors, multi-coreprocessors, coprocessing entities, application-specific instruction-setprocessors (ASIPs), microcontrollers, and/or controllers. Further, theprocessing element 205 may be embodied as one or more other processingdevices or circuitry. The term circuitry may refer to an entirelyhardware embodiment or a combination of hardware and computer programproducts. Thus, the processing element 205 may be embodied as integratedcircuits, application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), programmable logic arrays (PLAs),hardware accelerators, other circuitry, and/or the like. As willtherefore be understood, the processing element 205 may be configuredfor a particular use or configured to execute instructions stored involatile or non-volatile media or otherwise accessible to the processingelement 205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present disclosure when configured accordingly.

In particular embodiments, the analysis computing entity 105 may furtherinclude or be in communication with non-volatile media (also referred toas non-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In particular embodiments,the non-volatile storage or memory may include one or more non-volatilestorage or memory media 210, including but not limited to hard disks,ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipedememory, racetrack memory, and/or the like. As will be recognized, thenon-volatile storage or memory media may store databases (e.g.,parcel/item/shipment database), database instances, database managementsystems, data, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like. The term database,database instance, database management system, and/or similar terms usedherein interchangeably may refer to a collection of records orinformation/data that is stored in a computer-readable storage mediumusing one or more database models, such as a hierarchical databasemodel, network model, relational model, entity—relationship model,object model, document model, semantic model, graph model, and/or thelike.

In particular embodiments, the analysis computing entity 105 may furtherinclude or be in communication with volatile media (also referred to asvolatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In particular embodiments,the volatile storage or memory may also include one or more volatilestorage or memory media 215, including but not limited to RAM, DRAM,SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM,RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like. As will be recognized, the volatilestorage or memory media may be used to store at least portions of thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like being executed by, for example,the processing element 205. Thus, the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likemay be used to control certain aspects of the operation of the analysiscomputing entity 105 with the assistance of the processing element 205and operating system.

As indicated, in particular embodiments, the analysis computing entity105 may also include one or more communications interfaces 220 forcommunicating with various computing entities, such as by communicatinginformation/data, content, information/data, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired information/data transmission protocol, such asfiber distributed information/data interface (FDDI), digital subscriberline (DSL), Ethernet, asynchronous transfer mode (ATM), frame relay,information/data over cable service interface specification (DOCSIS), orany other wired transmission protocol. Similarly, the analysis computingentity 105 may be configured to communicate via wireless externalcommunication networks using any of a variety of protocols, such asgeneral packet radio service (GPRS), Universal Mobile TelecommunicationsSystem (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA20001x (1xRTT), Wideband Code Division Multiple Access (WCDMA), TimeDivision-Synchronous Code Division Multiple Access (TD-SCDMA), Long TermEvolution (LTE), Evolved Universal Terrestrial Radio Access Network(E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access(HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi),Wi-Fi Direct, 802.16 (WiMAX), ultra wideband (UWB), infrared (IR)protocols, near field communication (NFC) protocols, Wibree, Bluetoothprotocols, wireless universal serial bus (USB) protocols, long range lowpower (LoRa), LTE Cat M1, NarrowB and IoT (NB IoT), and/or any otherwireless protocol.

Although not shown, the analysis computing entity 105 may include or bein communication with one or more input elements, such as a keyboardinput, a mouse input, a touch screen/display input, motion input,movement input, audio input, pointing device input, joystick input,keypad input, and/or the like. The analysis computing entity 105 mayalso include or be in communication with one or more output elements(not shown), such as audio output, video output, screen/display output,motion output, movement output, and/or the like.

As will be appreciated, one or more of the analysis computing entity's100 components may be located remotely from other analysis computingentity 105 components, such as in a distributed system. Furthermore, oneor more of the components may be combined and additional componentsperforming functions described herein may be included in the analysiscomputing entity 105. Thus, the analysis computing entity 105 can beadapted to accommodate a variety of needs and circumstances. As will berecognized, these architectures and descriptions are provided forexemplary purposes only and are not limiting to the various embodiments.

2. Exemplary Mobile Computing Entities

Mobile computing entities 110 may be configured for autonomous operation(e.g., in association with an autonomous vehicle 140) and/or foroperation by a user (e.g., a vehicle operator, delivery personnel,customer, and/or the like). In certain embodiments, mobile computingentities 110 may be embodied as handheld computing entities, such asmobile phones, tablets, personal digital assistants, and/or the like,that may be operated at least in part based on user input received froma user via an input mechanism. Moreover, mobile computing entities 110may be embodied as onboard vehicle computing entities, such as centralvehicle electronic control units (ECUs), onboard multimedia system,and/or the like that may be operated at least in part based on userinput. Such onboard vehicle computing entities may be configured forautonomous and/or nearly autonomous operation however, as they may beembodied as onboard control systems for autonomous or semi-autonomousvehicles, such as unmanned aerial vehicles (UAVs), robots, and/or thelike. As a specific example, mobile computing entities 110 may beutilized as onboard controllers for UAVs configured for picking-upand/or delivering packages to various locations, and accordingly suchmobile computing entities 110 may be configured to monitor variousinputs (e.g., from various sensors) and generated various outputs (e.g.,control instructions received by various vehicle drive mechanisms). Itshould be understood that various embodiments of the present disclosuremay comprise a plurality of mobile computing entities 110 embodied inone or more forms (e.g., handheld mobile computing entities 110,vehicle-mounted mobile computing entities 110, and/or autonomous mobilecomputing entities 110).

As will be recognized, a user may be an individual, a family, a company,an organization, an entity, a department within an organization, arepresentative of an organization and/or person, and/or the like—whetheror not associated with a carrier. In particular embodiments, a user mayoperate a mobile computing entity 110 that may include one or morecomponents that are functionally similar to those of the analysiscomputing entity 105. FIG. 3 provides an illustrative schematicrepresentative of a mobile computing entity 110 that can be used inconjunction with embodiments of the present disclosure. In general, theterms device, system, computing entity, entity, and/or similar wordsused herein interchangeably may refer to, for example, one or morecomputers, computing entities, desktops, mobile phones, tablets,phablets, notebooks, laptops, distributed systems, vehicle multimediasystems, autonomous vehicle onboard control systems, watches, glasses,key fobs, radio frequency identification (RFID) tags, ear pieces,scanners, imaging devices/cameras (e.g., part of a multi-view imagecapture system), wristbands, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Mobile computing entities 110 can be operated by variousparties, including carrier personnel (sorters, loaders, deliverydrivers, network administrators, and/or the like). As shown in FIG. 3 ,the mobile computing entity 110 can include an antenna 312, atransmitter 304 (e.g., radio), a receiver 306 (e.g., radio), and aprocessing element 308 (e.g., CPLDs, microprocessors, multi-coreprocessors, coprocessing entities, ASIPs, microcontrollers, and/orcontrollers) that provides signals to and receives signals from thetransmitter 304 and receiver 306, respectively.

The signals provided to and received from the transmitter 304 and thereceiver 306, respectively, may include signaling information inaccordance with air interface standards of applicable wireless systems.In this regard, the mobile computing entity 110 may be capable ofoperating with one or more air interface standards, communicationprotocols, modulation types, and access types. More particularly, themobile computing entity 110 may operate in accordance with any of anumber of wireless communication standards and protocols, such as thosedescribed above with regard to the analysis computing entity 105. In aparticular embodiment, the mobile computing entity 110 may operate inaccordance with multiple wireless communication standards and protocols,such as UMTS, CDMA2000, 1xRTT, WCDMA, TD-SCDMA, LTE, E-UTRAN, EVDO,HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB,and/or the like. Similarly, the mobile computing entity 110 may operatein accordance with multiple wired communication standards and protocols,such as those described above with regard to the analysis computingentity 105 via a network interface 320.

Via these communication standards and protocols, the mobile computingentity 110 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service information/data (USSD),Short Message Service (SMS), Multimedia Messaging Service (MMS),Dual-Tone Multi-Frequency Signaling (DTMF), and/or Subscriber IdentityModule Dialer (SIM dialer). The mobile computing entity 110 can alsodownload changes, add-ons, and updates, for instance, to its firmware,software (e.g., including executable instructions, applications, programmodules), and operating system.

According to particular embodiments, the mobile computing entity 110 mayinclude location determining aspects, devices, modules, functionalities,and/or similar words used herein interchangeably. For example, themobile computing entity 110 may include outdoor positioning aspects,such as a location module adapted to acquire, for example, latitude,longitude, altitude, geocode, course, direction, heading, speed,universal time (UTC), date, and/or various other information/data. Inparticular embodiments, the location module can acquireinformation/data, sometimes known as ephemeris information/data, byidentifying the number of satellites in view and the relative positionsof those satellites (e.g., using global positioning systems (GPS)). Thesatellites may be a variety of different satellites, including Low EarthOrbit (LEO) satellite systems, Department of Defense (DOD) satellitesystems, the European Union Galileo positioning systems, the ChineseCompass navigation systems, Indian Regional Navigational satellitesystems, and/or the like. This information/data can be collected using avariety of coordinate systems, such as the Decimal Degrees (DD);Degrees, Minutes, Seconds (DMS); Universal Transverse Mercator (UTM);Universal Polar Stereographic (UPS) coordinate systems; and/or the like.Alternatively, the location information can be determined bytriangulating the mobile computing entity's 110 position in connectionwith a variety of other systems, including cellular towers, Wi-Fi accesspoints, and/or the like. Similarly, the mobile computing entity 110 mayinclude indoor positioning aspects, such as a location module adapted toacquire, for example, latitude, longitude, altitude, geocode, course,direction, heading, speed, time, date, and/or various otherinformation/data. Some of the indoor systems may use various position orlocation technologies including RFID tags, indoor beacons ortransmitters, Wi-Fi access points, cellular towers, nearby computingdevices/entities (e.g., smartphones, laptops) and/or the like. Forinstance, such technologies may include the iBeacons, Gimbal proximitybeacons, Bluetooth Low Energy (BLE) transmitters, NFC transmitters,and/or the like. These indoor positioning aspects can be used in avariety of settings to determine the location of someone or something towithin inches or centimeters.

The mobile computing entity 110 may also comprise a user interface (thatcan include a display 316 coupled to a processing element 308) and/or auser input interface (coupled to a processing element 308). For example,the user interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the mobile computing entity 110 to interact with and/orcause display of information from the analysis computing entity 105, asdescribed herein. The user input interface can comprise any of a numberof devices or interfaces allowing the mobile computing entity 110 toreceive information/data, such as a keypad 318 (hard or soft), a touchdisplay, voice/speech or motion interfaces, or other input device. Inembodiments including a keypad 318, the keypad 318 can include (or causedisplay of) the conventional numeric (0-9) and related keys (#, *), andother keys used for operating the mobile computing entity 110 and mayinclude a full set of alphabetic keys or set of keys that may beactivated to provide a full set of alphanumeric keys. In addition toproviding input, the user input interface can be used, for example, toactivate or deactivate certain functions, such as screen savers and/orsleep modes.

As shown in FIG. 3 , the mobile computing entity 110 may also include ancamera, imaging device, and/or similar words used herein interchangeably326 (e.g., still-image camera, video camera, IoT enabled camera, IoTmodule with a low resolution camera, a wireless enabled MCU, and/or thelike) configured to capture images. The mobile computing entity 110 maybe configured to capture images via the onboard camera 326, and to storethose imaging devices/cameras locally, such as in the volatile memory322 and/or non-volatile memory 324. As discussed herein, the mobilecomputing entity 110 may be further configured to match the capturedimage data with relevant location and/or time information captured viathe location determining aspects to provide contextual information/data,such as a time-stamp, date-stamp, location-stamp, and/or the like to theimage data reflective of the time, date, and/or location at which theimage data was captured via the camera 326. The contextual data may bestored as a portion of the image (such that a visual representation ofthe image data includes the contextual data) and/or may be stored asmetadata associated with the image data that may be accessible tovarious computing entities 110.

The mobile computing entity 110 may include other input mechanisms, suchas scanners (e.g., barcode scanners), microphones, accelerometers, RFIDreaders, and/or the like configured to capture and store variousinformation types for the mobile computing entity 110. For example, ascanner may be used to capture parcel/item/shipment information/datafrom an item indicator disposed on a surface of a shipment or otheritem. In certain embodiments, the mobile computing entity 110 may beconfigured to associate any captured input information/data, forexample, via the onboard processing element 308. For example, scan datacaptured via a scanner may be associated with image data captured viathe camera 326 such that the scan data is provided as contextual dataassociated with the image data.

The mobile computing entity 110 can also include volatile storage ormemory 322 and/or non-volatile storage or memory 324, which can beembedded and/or may be removable. For example, the non-volatile memorymay be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards,Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM,Millipede memory, racetrack memory, and/or the like. The volatile memorymay be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management systems, information/data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like to implement the functions of the mobile computing entity 110.As indicated, this may include a user application that is resident onthe entity or accessible through a browser or other user interface forcommunicating with the analysis computing entity 105 and/or variousother computing entities.

In another embodiment, the mobile computing entity 110 may include oneor more components or functionality that are the same or similar tothose of the analysis computing entity 105, as described in greaterdetail above. As will be recognized, these architectures anddescriptions are provided for exemplary purposes only and are notlimiting to the various embodiments.

3. Exemplary Autonomous Vehicle

As utilized herein, autonomous vehicles 140 may be configured fortransporting one or more shipments/items (e.g., one or more packages,parcels, bags, containers, loads, crates, items banded together, vehicleparts, pallets, drums, the like, and/or similar words used hereininterchangeably). Various autonomous vehicles 140 may be configured asdiscussed in co-pending U.S. patent application Ser. No. 15/582,129,filed Apr. 28, 2017, and incorporated herein by reference in itsentirety.

In certain embodiments, each autonomous vehicle 140 may be associatedwith a unique vehicle identifier (such as a vehicle ID) that uniquelyidentifies the autonomous vehicle 140. The unique vehicle ID may includecharacters, such as numbers, letters, symbols, and/or the like. Forexample, an alphanumeric vehicle ID (e.g., “AS445”) may be associatedwith each vehicle 140. Although the autonomous vehicles 140 arediscussed herein as comprising unmanned aerial vehicles (UAVs), itshould be understood that the autonomous vehicles may compriseground-based autonomous vehicles 140 in certain embodiments.

FIG. 4 illustrates an example autonomous vehicle 140 that may beutilized in various embodiments. As shown in FIG. 4 , the autonomousvehicle 140 is embodied as a UAV generally comprising a UAV chassis 142and a plurality of propulsion members 143 extending outwardly from theUAV chassis 142 (in certain embodiments, the propulsion members aresurrounded by propeller guards 141). The UAV chassis 142 generallydefines a body of the UAV, which the propulsion members 143 (e.g.,propellers having a plurality of blades configured for rotating within apropeller guard circumscribing the propellers) are configured to liftand guide during flight. According to various embodiments, the UAVchassis 142 may be formed from any material of suitable strength andweight (including sustainable and reusable materials), including but notlimited to composite materials, aluminum, titanium, polymers, and/or thelike, and can be formed through any suitable process.

In the embodiment depicted in FIG. 4 , the autonomous vehicle 140 is ahexacopter and includes six separate propulsion members 143, eachextending outwardly from the UAV chassis 142. However, as will beappreciated from the description herein, the autonomous vehicle 140 mayinclude any number of propulsion members 143 suitable to provide liftand guide the autonomous vehicle 140 during flight. The propulsionmembers 143 are configured to enable vertical locomotion (e.g., lift)and/or horizontal locomotion, as shown in the example embodiment of FIG.4 , as well as enabling roll, pitch, and yaw movements of the autonomousvehicle 140. Although not shown, it should be understood that autonomousvehicles 140 may comprise any of a variety of propulsion mechanisms,such as balloon-based lift mechanisms (e.g., enabling lighter-than-airtransportation), wing-based lift mechanisms, turbine-based liftmechanisms, and/or the like.

In the illustrated embodiment, the propulsion members 143 areelectrically powered (e.g., by an electric motor that controls the speedat which the propellers rotate). However, as will be recognized, thepropulsion members 143 may be powered by internal combustion engines(e.g., alcohol-fueled, oil-fueled, gasoline-fueled, and/or the like)driving an alternator, hydrogen fuel-cells, and/or the like.

Moreover, as shown in FIG. 4 , the lower portion of the UAV chassis 142is configured to receive and engage a parcel carrier 144 configured forselectively supporting a parcel/item/shipment to be delivered from amanual delivery vehicle 100 to a delivery destination. The parcelcarrier 144 may define the lowest point of the autonomous vehicle 140when secured relative to the chassis 142 of the autonomous vehicle 140,such that a parcel/item/shipment carried by the autonomous vehicle 140may be positioned below the chassis of the autonomous vehicle 140 duringtransit. In certain embodiments, the parcel carrier 144 may comprise oneor more parcel engagement arms 145 configured to detachably secure aparcel/item/shipment relative to the autonomous vehicle 140. In suchembodiments, the parcel/item/shipment may be suspended by the parcelengagement arms 145 below the autonomous vehicle 140, such that it maybe released from the autonomous vehicle 140 while the autonomous vehicle140 hovers over a desired delivery destination. However, it should beunderstood that the parcel carrier 144 may have any of a variety ofconfigurations enabling the autonomous vehicle 140 to support aparcel/item/shipment during transit. For example, the parcel carrier 144may comprise a parcel cage for enclosing a parcel/item/shipment duringtransit, a parcel platform positioned above the UAV chassis 142, and/orthe like.

In certain embodiments, the parcel carrier 144 may be detachably securedrelative to the UAV chassis 142, for example, such that alternativeparcel carriers 144 having shipments/items secured thereto may bealternatively connected relative to the UAV chassis 142 for delivery. Incertain embodiments, a UAV may be configured to deliver aparcel/item/shipment secured within a parcel carrier 144, and return toa manual delivery vehicle 100 where the now-empty parcel carrier 144(due to the delivery of the parcel/item/shipment that was previouslysecured therein) may be detached from the autonomous vehicle 140 and anew parcel carrier 144 having a second parcel/item/shipment may securedto the UAV chassis 142.

As indicated by FIG. 5 , which illustrates an example manual deliveryvehicle 100 according to various embodiments, the autonomous vehicle 140may be configured to selectively engage a portion of the manual deliveryvehicle 100 such that the manual delivery vehicle 100 may transport theautonomous vehicle 140 and/or other similar autonomous vehicles. Forexample, the UAV chassis 142 may be configured to engage one or morevehicle guide mechanisms secured relative to the manual delivery vehicle100 to detachably secure the autonomous vehicle 140 relative to themanual delivery vehicle 100 when not delivering shipments/items. Asdiscussed herein, the guide mechanism of a manual delivery vehicle 100may be configured to enable an autonomous vehicle 140 to autonomouslytake-off or depart from the manual delivery vehicle 100 to initiate adelivery activity and/or to autonomously land or arrive at the manualdelivery vehicle 100 to conclude a delivery activity.

Moreover, the autonomous vehicle 140 additionally comprises an onboardcontrol system embodied as a mobile computing entity 110 that includes aplurality of sensing devices that assist in navigating autonomousvehicle 140 during flight. For example, the control system is configuredto control movement of the vehicle 140, navigation of the vehicle 140,obstacle avoidance, item delivery, and/or the like. Although not shown,the control system may additionally comprise one or more userinterfaces, which may comprise an output mechanism and/or an inputmechanism configured to receive user input. For example, the userinterface may be configured to enable autonomous vehicle technicians toreview diagnostic information/data relating to the autonomous vehicle140, and/or a user of the autonomous vehicle 140 may utilize the userinterface to input and/or review information/data indicative of adestination location for the autonomous vehicle 140.

The plurality of sensing devices are configured to detect objects aroundthe autonomous vehicle 140 and provide feedback to an autonomous vehicleonboard control system to assist in guiding the autonomous vehicle 140in the execution of various operations, such as takeoff, flightnavigation, and landing, as will be described in greater detail herein.In certain embodiments, the autonomous vehicle control system comprisesa plurality of sensors including ground landing sensors, vehicle landingsensors, flight guidance sensors, and one or more imagingdevices/cameras (e.g., that utilize object recognition algorithms toidentify objects). The vehicle landing sensors may be positioned on alower portion of the UAV chassis 142 and assist in landing theautonomous vehicle 140 on a manual delivery vehicle 100 (e.g., as shownin FIG. 5 ) as will be described in greater detail herein. The vehiclelanding sensors may include one or more imaging devices/cameras (e.g.,video imaging devices/cameras and/or still imaging devices/cameras), oneor more altitude sensors (e.g., Light Detection and Ranging (LIDAR)sensors, laser-based distance sensors, infrared distance sensors,ultrasonic distance sensors, optical sensors and/or the like). Beinglocated on the lower portion of the UAV chassis 142, the vehicle landingsensors are positioned below the propulsion members 143 and have a lineof sight with the manual delivery vehicle's UAV support mechanism (FIG.5 ) when the autonomous vehicle 140 approaches the manual deliveryvehicle 100 during landing.

The autonomous vehicle's one or more imaging devices/cameras may also bepositioned on the lower portion of the UAV chassis 142, on propellerguards 141, and/or the like. The one or more imaging devices/cameras mayinclude video and/or still imaging devices/cameras, and may captureimages and/or video of the flight of the autonomous vehicle 140 during adelivery process, and may assist in verifying or confirming delivery ofa parcel/item/shipment to a destination, as will be described in greaterdetail herein. Being located on the lower portion of the UAV chassis142, the one or more imaging devices/cameras are positioned below thepropulsion members 143 and have an unobstructed line of sight to viewthe flight of the autonomous vehicle 140. Moreover, as discussedspecifically in reference to the various mobile computing entities 110,the one or more imaging devices/cameras disposed on the UAV may beconfigured for capturing images of one or more items/shipments beforepicking-up those items/shipments, after dropping off thoseitems/shipments, during transit of the items/shipments, and/or the like.

In various embodiments, the control system of the autonomous vehicle 140may encompass, for example, an information/data collection devicesimilar to information/data collection device 130 discussed in referenceto a manual delivery vehicle 100 or other computing entities.

In particular embodiments, the information/data collection device 130may include, be associated with, or be in wired or wirelesscommunication with one or more processors (various exemplary processorsare described in greater detail below), one or more location-determiningdevices or one or more location sensors (e.g., Global NavigationSatellite System (GNSS) sensors, indoor location sensors, (e.g.,Bluetooth sensors, Wi-Fi sensors, GPS sensors, beacon sensors, and/orthe like), one or more real-time clocks, a J-Bus protocol architecture,one or more electronic control modules (ECM), one or more communicationports for receiving information/data from various sensors (e.g., via aCAN-bus), one or more communication ports for transmitting/sendinginformation/data, one or more RFID tags/sensors, one or more powersources, one or more information/data radios for communication with avariety of communication networks, one or more memory modules, and oneor more programmable logic controllers (PLC). It should be noted thatmany of these components may be located in the autonomous vehicle 140but external to the information/data collection device 130.

In some embodiments, the one or more location sensors, modules, orsimilar words used herein interchangeably may be one of severalcomponents in wired or wireless communication with or available to theinformation/data collection device 130. Moreover, the one or morelocation sensors may be compatible with GPS satellites 112, such as LowEarth Orbit (LEO) satellite systems, Department of Defense (DOD)satellite systems, the European Union Galileo positioning systems, theChinese Compass navigation systems, Indian Regional Navigationalsatellite systems, and/or the like. This information/data can becollected using a variety of coordinate systems, such as the DecimalDegrees (DD); Degrees, Minutes, Seconds (DMS); Universal TransverseMercator (UTM); Universal Polar Stereographic (UPS) coordinate systems;and/or the like.

As discussed herein, triangulation and/or proximity based locationdeterminations may be used in connection with a device associated with aparticular autonomous vehicle 140 and with various communication points(e.g., cellular towers, Wi-Fi access points, and/or the like) positionedat various locations throughout a geographic area to monitor thelocation of the vehicle 100 and/or its operator. The one or morelocation sensors may be used to receive latitude, longitude, altitude,heading or direction, geocode, course, position, time, locationidentifying information/data, and/or speed information/data (e.g.,referred to herein as location information/data and further describedherein below). The one or more location sensors may also communicatewith the analysis computing entity 105, the information/data collectiondevice 130, mobile computing entity 110, and/or similar computingentities.

In some embodiments, the ECM may be one of several components incommunication with and/or available to the information/data collectiondevice 130. The ECM, which may be a scalable and subservient device tothe information/data collection device 130, may have information/dataprocessing capability to decode and store analog and digital inputsreceived from, for example, vehicle systems and sensors. The ECM mayfurther have information/data processing capability to collect andpresent location information/data to the J-Bus (which may allowtransmission to the information/data collection device 130), and outputlocation identifying information/data, for example, via a display and/orother output device (e.g., a speaker).

As indicated, a communication port may be one of several componentsavailable in the information/data collection device 130 (or be in or asa separate computing entity). Embodiments of the communication port mayinclude an Infrared information/data Association (IrDA) communicationport, an information/data radio, and/or a serial port. The communicationport may receive instructions for the information/data collection device130. These instructions may be specific to the vehicle 100 in which theinformation/data collection device 130 is installed, specific to thegeographic area and/or serviceable point to which the vehicle 100 willbe traveling, specific to the function the vehicle serves within afleet, and/or the like. In particular embodiments, the information/dataradio may be configured to communicate with a WWAN, WLAN, WPAN, or anycombination thereof. For example, the information/data radio maycommunicate via various wireless protocols, such as 802.11, GPRS, UMTS,CDMA2000, 1xRTT, WCDMA, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA,Wi-Fi, WiMAX, UWB, IR protocols, Bluetooth protocols (including BLE),wireless USB protocols, and/or any other wireless protocol. As yet otherexamples, the communication port may be configured to transmit and/orreceive information/data transmissions via light-based communicationprotocols (e.g., utilizing specific light emission frequencies,wavelengths (e.g., visible light, infrared light, and/or the like),and/or the like to transmit data), via sound-based communicationprotocols (e.g., utilizing specific sound frequencies to transmit data),and/or the like.

4. Exemplary Manual Delivery Vehicle

As discussed herein, a manual delivery vehicle 100 may be a user (e.g.,human) operable delivery vehicle configured for transporting a vehicleoperator, a plurality of items, and one or more autonomous vehicles 140along a delivery route. However, it should be understood that in certainembodiments, even though the term manual delivery vehicle 100 is used,this is simply to distinguish it in the description from the autonomousvehicle 140. Thus, the manual delivery vehicle 100 may itself beautonomous or semi-autonomous. For example, the manual delivery vehicle100 is a self-driving vehicle in some embodiments such that no physicalperson or user is needed to operate the vehicle 100. In certainembodiments, an autonomous manual delivery vehicle 100 may be configuredas an autonomous base vehicle configured to carry a plurality of items,one or more smaller, auxiliary autonomous vehicles (e.g., autonomousvehicles 140 described in detail herein), a human delivery personnel(e.g., who may complete various deliveries from the manual deliveryvehicle 100 to respective destination locations), and/or the like. Forexample, a vehicle 100 may be a manned or an unmanned tractor, truck,car, motorcycle, moped, Segway, bicycle, golf cart, hand truck, cart,trailer, tractor and trailer combination, van, flatbed truck, vehicle,drone, airplane, helicopter, boat, barge, and/or any other form ofobject for moving or transporting people, UAVs, and/or shipments/items(e.g., one or more packages, parcels, bags, containers, loads, crates,items banded together, vehicle parts, pallets, drums, the like, and/orsimilar words used herein interchangeably). In particular embodiments,each vehicle 100 may be associated with a unique vehicle identifier(such as a vehicle ID) that uniquely identifies the vehicle 100. Theunique vehicle ID (e.g., trailer ID, tractor ID, vehicle ID, and/or thelike) may include characters, such as numbers, letters, symbols, and/orthe like. For example, an alphanumeric vehicle ID (e.g., “AS445”) may beassociated with each vehicle 100. In another embodiment, the uniquevehicle ID may be the license plate, registration number, or otheridentifying information/data assigned to the vehicle 100. In variousembodiments, the manual delivery vehicle 100 may be configured asdiscussed in co-pending U.S. patent application Ser. No. 15/582,129,filed Apr. 28, 2017, and incorporated herein by reference in itsentirety.

In various embodiments, the manual delivery vehicle 100 comprises one ormore autonomous vehicle support mechanisms, as shown in FIG. 5 . Theautonomous vehicle support mechanisms may be configured to enable theautonomous vehicles 140 to launch and land at the manual deliveryvehicle 100 while completing autonomous deliveries. In certainembodiments, the autonomous vehicle support mechanisms may be configuredto enable the autonomous vehicles 140 to launch and/or land while themanual delivery vehicle 100 is moving, however certain embodiments maybe configured to enable autonomous vehicle 140 launching and/or landingwhile the manual delivery vehicle 100 is stationary.

Moreover, although not shown, the interior of the manual deliveryvehicle 100 may comprise a cargo area configured for storing a pluralityof items, a plurality of autonomous vehicles 140, a plurality ofautonomous vehicle components, and/or the like. In certain embodiments,items designated for autonomous delivery may be stored in one or moreautonomously operated storage assemblies within the cargo area of themanual delivery vehicle 100. When a particular parcel/item/shipment isidentified as ready for delivery, the storage assembly autonomouslydelivers the parcel/item/shipment to an autonomous vehicle 140 fordelivery.

Moreover, the manual delivery vehicle 100 may comprise and/or beassociated with one or more mobile computing entities 110, devices,and/or similar words used herein interchangeably. The mobile computingentities 110 may comprise, for example, an information/data collectiondevice 130 or other computing entities.

In particular embodiments, the information/data collection device 130may include, be associated with, or be in wired or wirelesscommunication with one or more processors (various exemplary processorsare described in greater detail below), one or more location-determiningdevices or one or more location sensors (e.g., GNSS sensors), one ormore telematics sensors, one or more real-time clocks, a J-Bus protocolarchitecture, one or more ECMs, one or more communication ports forreceiving telematics information/data from various sensors (e.g., via aCAN-bus), one or more communication ports for transmitting/sendinginformation/data, one or more RFID tags/sensors, one or more powersources, one or more information/data radios for communication with avariety of communication networks, one or more memory modules, and oneor more programmable logic controllers (PLC). It should be noted thatmany of these components may be located in the vehicle 100 but externalto the information/data collection device 130.

In particular embodiments, the one or more location sensors, modules, orsimilar words used herein interchangeably may be one of severalcomponents in wired or wireless communication with or available to theinformation/data collection device 130. Moreover, the one or morelocation sensors may be compatible with GPS satellites 112, LEOsatellite systems, DOD satellite systems, the European Union Galileopositioning systems, the Chinese Compass navigation systems, IndianRegional Navigational satellite systems, and/or the like, as discussedabove in reference to the autonomous delivery vehicle. Alternatively,triangulation may be used in connection with a device associated with aparticular vehicle and/or the vehicle's operator and with variouscommunication points (e.g., cellular towers or Wi-Fi access points)positioned at various locations throughout a geographic area to monitorthe location of the vehicle 100 and/or its operator. The one or morelocation sensors may be used to receive latitude, longitude, altitude,heading or direction, geocode, course, position, time, and/or speedinformation/data (e.g., referred to herein as telematicsinformation/data and further described herein below). The one or morelocation sensors may also communicate with the analysis computing entity105, the information/data collection device 130, mobile computing entity110, and/or similar computing entities.

In particular embodiments, the ECM may be one of several components incommunication with and/or available to the information/data collectiondevice 130. The ECM, which may be a scalable and subservient device tothe information/data collection device 130, may have information/dataprocessing capability to decode and store analog and digital inputs fromvehicle systems and sensors (e.g., location sensor). The ECM may furtherhave information/data processing capability to collect and presentcollected information/data to the J-Bus (which may allow transmission tothe information/data collection device 130).

As indicated, a communication port may be one of several componentsavailable in the information/data collection device 130 (or be in or asa separate computing entity). Embodiments of the communication port mayinclude an IrDA communication port, an information/data radio, and/or aserial port. The communication port may receive instructions for theinformation/data collection device 130. These instructions may bespecific to the vehicle 100 in which the information/data collectiondevice 130 is installed, specific to the geographic area in which thevehicle 100 will be traveling, specific to the function the vehicle 100serves within a fleet, and/or the like. In particular embodiments, theinformation/data radio may be configured to communicate with WWAN, WLAN,WPAN, or any combination thereof, as discussed in reference to theautonomous vehicle, above.

5. Exemplary Parcel/Item/Shipment

In particular embodiments, each parcel/item/shipment may include and/orbe associated with a parcel/item/shipment identifier, such as analphanumeric identifier. Such parcel/item/shipment identifiers may berepresented as text, barcodes, tags, character strings, Aztec Codes,MaxiCodes, information/data Matrices, Quick Response (QR) Codes,electronic representations, and/or the like. A uniqueparcel/item/shipment identifier (e.g., 123456789) may be used by thecarrier to identify and track the parcel/item/shipment as it movesthrough the carrier's transportation network and to associate aparticular physical parcel/item/shipment with an electronically storedparcel/item/shipment profile. For example, the parcel/item/shipmentprofile may be stored in a parcel/item/shipment level detail database,and may store data informing various carrier personnel and/or deliveryvehicles (e.g., autonomous vehicle 140) of delivery-relatedinformation/data specific to a particular shipment. Further, suchparcel/item/shipment identifiers can be affixed to shipments/items by,for example, using a sticker (e.g., label) with the uniqueparcel/item/shipment identifier printed thereon (in human and/or machinereadable form) or an RFID tag with the unique parcel/item/shipmentidentifier stored therein. Such items may be referred to as “connected”shipments/items and/or “non-connected” shipments/items.

In particular embodiments, connected shipments/items include the abilityto determine their locations and/or communicate with various computingentities. This may include the parcel/item/shipment being able tocommunicate via a chip or other devices, such as an integrated circuitchip, RFID technology, NFC technology, Bluetooth technology, Wi-Fitechnology, light-based communication protocols, sound-basedcommunication protocols, and any other suitable communicationtechniques, standards, or protocols with one another and/or communicatewith various computing entities for a variety of purposes. Connectedshipments/items may include one or more components that are functionallysimilar to those of the analysis computing entity 105 and/or mobilecomputing entity 110 as described herein. For example, in particularembodiments, each connected parcel/item/shipment may include one or moreprocessing elements, one or more display device/input devices (e.g.,including user interfaces), volatile and non-volatile storage or memory,and/or one or more communications interfaces. In this regard, in someexample embodiments, a parcel/item/shipment may communicate send “to”address information/data, received “from” address information/data,unique identifier codes, location information/data, statusinformation/data, and/or various other information/data.

In particular embodiments, non-connected shipments/items do nottypically include the ability to determine their locations and/or mightnot be able communicate with various computing entities or are notdesignated to do so by the carrier. The location of non-connectedshipments/items can be determined with the aid of other appropriatecomputing entities. For example, non-connected shipments/items can bescanned (e.g., affixed barcodes, RFID tags, and/or the like) or have thecontainers or vehicles in which they are located scanned or located. Aswill be recognized, an actual scan or location determination of aparcel/item/shipment is not necessarily required to determine thelocation of a parcel/item/shipment. That is, a scanning operation mightnot actually be performed on a label affixed directly to aparcel/item/shipment or location determination might not be madespecifically for or by a parcel/item/shipment. For example, a label on alarger container housing many shipments/items can be scanned, and byassociation, the location of the shipments/items housed within thecontainer are considered to be located in the container at the scannedlocation. Similarly, the location of a vehicle transporting manyshipments/items can be determined, and by association, the location ofthe shipments/items being transported by the vehicle are considered tobe located in the vehicle 100 at the determined location. These can bereferred to as “logical” scans/determinations or “virtual”scans/determinations. Thus, the location of the shipments/items is basedon the assumption they are within the container or vehicle, despite thefact that one or more of such shipments/items might not actually bethere.

6. Exemplary Parcel/Item/Shipment Profile

As noted herein, various shipments/items may have an associatedparcel/item/shipment profile, record, and/or similar words used hereininterchangeably stored in a parcel/item/shipment detail database. Theparcel/item/shipment profile may be utilized by the carrier to track thecurrent location of the parcel/item/shipment and to store and retrieveinformation/data about the parcel/item/shipment. For example, theparcel/item/shipment profile may comprise electronic data correspondingto the associated parcel/item/shipment, and may identify variousshipping instructions for the parcel/item/shipment, variouscharacteristics of the parcel/item/shipment, and/or the like. Theelectronic data may be in a format readable by various computingentities, such as an analysis computing entity 105, a mobile computingentity 110, an autonomous vehicle control system, and/or the like.However, it should be understood that a computing entity configured forselectively retrieving electronic data within variousparcel/item/shipment profiles may comprise a format conversion aspectconfigured to reformat requested data to be readable by a requestingcomputing entity.

In various embodiments, the parcel/item/shipment profile comprisesidentifying information/data corresponding to the parcel/item/shipment.The identifying information/data may comprise information/dataidentifying the unique parcel/item/shipment identifier associated withthe parcel/item/shipment. Accordingly, upon providing the identifyinginformation/data to the parcel/item/shipment detail database, theparcel/item/shipment detail database or other data store may query thestored parcel/item/shipment profiles to retrieve theparcel/item/shipment profile corresponding to the provided uniqueidentifier.

Moreover, the parcel/item/shipment profiles may comprise shippinginformation/data for the parcel/item/shipment. For example, the shippinginformation/data may identify an origin location (e.g., an originserviceable point), a destination location (e.g., a destinationserviceable point), a service level (e.g., Next Day Air, Overnight,Express, Next Day Air Early AM, Next Day Air Saver, Jetline, Sprintline,Secureline, 2nd Day Air, Priority, 2nd Day Air Early AM, 3 Day Select,Ground, Standard, First Class, Media Mail, SurePost, Freight, High valueCHC (critical health care) shipments, and/or the like), whether adelivery confirmation signature is required, and/or the like. In certainembodiments, at least a portion of the shipping information/data may beutilized as identifying information/data to identify aparcel/item/shipment. For example, a destination location may beutilized to query the parcel/item/shipment detail database to retrievedata about the parcel/item/shipment.

In certain embodiments, the parcel/item/shipment profile comprisescharacteristic information/ data identifying parcel/item/shipmentcharacteristics. For example, the characteristic information/ data mayidentify dimensions of the parcel/item/shipment (e.g., length, width,height), a weight of the parcel/item/shipment, contents of theparcel/item/shipment, and/or the like. In certain embodiments, thecontents of the parcel/item/shipment may comprise a precise listing ofthe contents of the parcel/item/shipment (e.g., three widgets) and/orthe contents may identify whether the parcel/item/shipment contains anyhazardous materials (e.g., the contents may indicate whether theparcel/item/shipment contains one or more of the following: no hazardousmaterials, toxic materials, flammable materials, pressurized materials,controlled substances, firearms, and/or the like).

7. Exemplary Conveying Mechanism

As shipments/items are moved through a carrier's logistics networkbetween corresponding origins and destinations, those shipments/itemsmay pass through one or more carrier sort locations. Each carrier sortlocation may comprise one or more conveying mechanisms (e.g., conveyorbelts, chutes, and/or the like, configured to move shipments/itemsbetween incoming locations (e.g., incoming vehicles) to correspondingoutbound vehicles destined for later locations along aparcel/item/shipment's intended transportation path between the originand destination.

FIG. 6 includes an illustration of a conveying mechanism 115 accordingto particular embodiments of the present disclosure. As shown in FIGS.6A and 6B, the conveying mechanism 115 may comprise a multi-view imagecapture system=(comprising one or more image/acquisition devices 401and/or similar words used herein interchangeably) for acquiringinformation/data (including image information/data) from aparcel/item/shipment. As mentioned herein, each parcel/item/shipment mayinclude a parcel/item/shipment identifier, such as an alphanumericidentifier. Such parcel/item/shipment identifiers may be represented astext, barcodes, Aztec Codes, MaxiCodes, Data Matrices, Quick Response(QR) Codes, electronic representations, tags, character strings, and/orthe like. The unique parcel/item/shipment identifier (e.g., 123456789)may be used by the carrier to identify and track theparcel/item/shipment as it moves through the carrier's transportationnetwork. Further, such parcel/item/shipment identifiers can be affixedto items by, for example, using a sticker (e.g., label) with the uniqueparcel/item/shipment identifier printed thereon (in human and/or machinereadable form) or an RFID tag with the unique parcel/item/shipmentidentifier stored therein. Accordingly, the one or moreimage/acquisition devices 401 may be capable of acquiring data(including parcel/item/shipment identifiers) relevant to eachparcel/item/shipment, including parcel/item/shipment identifierinformation/data, parcel/item/shipment condition information/data,and/or the like for shipments/items traveling along a correspondingconveying mechanism 115 (e.g., conveyor belt, slide, chute, bottleconveyor, open or enclosed track conveyor, I-beam conveyor, cleatedconveyor, and/or the like).

As indicated, the image/acquisition devices 401 may be part of amulti-view image capture system 400 configured to capture images (e.g.,image information/data) of shipments/items (and/or parcel/item/shipmentidentifiers) moving along the conveying mechanism 115. For example, theimage/acquisition device 401 may include or be a video camera,camcorder, still camera, web camera, Single-Lens Reflex (SLR) camera,high-speed camera, and/or the like. In various embodiments, theimage/acquisition device 401 may be configured to record high-resolutionimage data and/or to capture image data at a high speed (e.g., utilizinga frame rate of at least 60 frames per second). Alternatively, theimage/acquisition device 401 may be configured to record low-resolutionimage data (e.g., images comprising less than 480 horizontal scan lines)and/or to capture image data at a low speed (e.g., utilizing a framerate less than 60 frames per second). As will be understood by thoseskilled in the art, the image/acquisition device 401 may be configuredto operate with various combinations of the above features (e.g.,capturing images with less than 480 horizontal scan lines and utilizinga frame rate of at least 60 frames per second, or capturing images withat least 480 horizontal scan lines and utilizing a frame rate less than60 frames per second). In various embodiments, the image/acquisitiondevice 401 may be configured to capture image data of theshipments/items and conveying mechanism 115 of sufficient quality that auser viewing the image data on the display can identify eachparcel/item/shipment represented in the displayed image data. Forexample, in embodiments wherein the conveying mechanism 115 andshipments/items are moving at a high rate of speed, theimage/acquisition device 401 may be configured to capture image data ata high speed. As will be recognized, the image data can be captured inor converted to a variety of formats, such as Joint Photographic ExpertsGroup (JPEG), Motion JPEG (MJPEG), Moving Picture Experts Group (MPEG),Graphics Interchange Format (GIF), Portable Network Graphics (PNG),Tagged Image File Format (TIFF), bitmap (BMP), H.264, H.263, Flash Video(FLV), Hypertext Markup Language 5 (HTMLS), VP6, VP8, and/or the like.In certain embodiments, various features (e.g., text, objects ofinterest, codes, parcel/item/shipment identifiers, and/or the like) canbe extracted from the image data.

As described in more detail with respect to FIG. 7 herein, in someembodiments, the image capture system 400 may alternatively or identicalimage capture systems may additionally be located within various otherpoints or areas within a parcel carrier's logistic network other thanthe environment associated with FIG. 6A.

The image/acquisition device 401 may additionally include or be one ormore scanners, readers, interrogators, and similar words used hereininterchangeably configured for capturing parcel/item/shipment indiciafor each parcel/item/shipment (e.g., including parcel/item/shipmentidentifiers). For example, the scanners may include a barcode scanner,an RFID reader, and/or the like configured to recognize and identifyparcel/item/shipment identifiers associated with eachparcel/item/shipment. In particular embodiments, the image/acquisitiondevice 401 may be capable of receiving visible light, infrared light,radio transmissions, and/or other transmissions capable of transmittinginformation to the image/acquisition device 401. Similarly, theimage/acquisition device 401 may include or be used in association withvarious lighting, such as light emitting diodes (LEDs), Infrared lights,array lights, strobe lights, and/or other lighting mechanisms tosufficiently illuminate the zones of interest to capture image data foranalysis.

Similar to mobile computing entities 110 described above, in particularembodiments, the conveying mechanism 115, multi-view image capturesystem 400, and/or image/acquisition devices 401 may also include one ormore communications interfaces for communicating with various computingentities, such as by communicating information/data, content,information/data, and/or similar terms used herein interchangeably thatcan be transmitted, received, operated on, processed, displayed, stored,and/or the like. Such communication may be executed using a wired datatransmission protocol, such as FDDI, DSL, Ethernet, ATM, frame relay,DOCSIS, or any other wired transmission protocol. Similarly, theconveying mechanism 115 may be configured to communicate via wirelessexternal communication networks using any of a variety of protocols,such as GPRS, UMTS, CDMA2000, 1xRTT, WCDMA, TD-SCDMA, LTE, E-UTRAN,EVDO, HSPA, HSDPA, Wi-Fi, WiMAX, UWB, IR protocols, NFC protocols,Bluetooth™ protocols, wireless USB protocols, long range low power(LoRa), LTE Cat M1, NarrowBand IoT (NB IoT), and/or any other wirelessprotocol.

As will be understood by those skilled in the art, the multi-view imagecapture system 400 may include more than one image/acquisition device401 (see FIG. 6B). In various embodiments, one or more additionalimage/acquisition devices may be used to capture additional image dataat one or more additional locations along the conveying mechanism 115 oran additional conveying mechanism. Such additional image/acquisitiondevices 401 may be located, for example, after the flow of items alongthe conveying mechanism 115 is disturbed (e.g., the flow ofshipments/items is culled, merged with an additional flow ofshipments/items, or diverted to an additional conveying mechanism).Alternatively, one or more additional image/acquisition devices may belocated along the conveying mechanism 115, such that the one or moreadditional image/acquisition devices may capture updated image dataafter one or more of the shipments/items may have been removed from theconveying mechanism 115. In various embodiments, the one or moreadditional image/acquisition devices may include componentssubstantially similar to the image/acquisition device 401. For example,the one or more additional image/acquisition devices may include or beassociated with one or more imaging devices and one or more scanners,readers, interrogators, and similar words used herein interchangeably,as described above in regards to the image/acquisition device 401.However, the one or more additional image/acquisition devices mayinclude fewer components than image/acquisition device 401. For example,the one or more additional image/acquisition devices may not include ascanner, reader, interrogator, or similar words used herein, and may beconfigured to receive parcel/item/shipment identifiers from theimage/acquisition device 401.

IV. Exemplary System Operation

Existing and conventional technologies fail to capture images ofobjects, generate damage data, such as the damage analyses describedherein, and/or make various modifications based on the damage data. Forexample, some technologies, such as IoT devices (e.g., smart speakers)fail to include image capturing devices and back-end systems thatdetermine whether damage to parcels have occurred. Although some IoTdevices can cause an altering of devices (e.g., a smart thermostat)based on receiving user voice input, these IoT devices are not yet ableto modify conditions (e.g., slow/halt an autonomous vehicle) in responseto detecting damage of one or more parcels along a transit route (e.g.,the transit route 700 of FIG. 7 ). As described above, some particulartechnologies in the shipping industry only include passive softwareapplications that receive user input to identify whether one or moreparcels are damaged. However, these applications fail to employ machinelearning and other functionalities to help detect and analyze damage toparcels.

Various embodiments of the present disclosure improve these existingtechnologies in at least the following ways. After one or more digitalimages are received, some embodiments allow a feeding of the one or moredigital images through one or more machine learning models in order topredict or classify (with more accuracy than existing softwareapplications) whether one or more parcels represented in the one or moredigital images have incurred damage, belong to a particular category ofdamage, and/or other functionalities associated with the damage (e.g.,mitigation instructions). Some embodiments, also address theshortcomings of IoT devices, by providing a signal (e.g., a controlsignal) to one or more computing devices based on damage analyzation.The signal may cause the computing device itself and/or a condition(e.g., temperature in a vehicle) to be modified.

In some embodiments of the present disclosure, several digital images ofa single parcel/item/shipment can be captured, at or along points in atransportation and logistics network, from various angles such thatseveral fields of view are represented (e.g., a top, frontal, side, andbottom view). The images of a single parcel at each single point arecombined and fed into a machine learning model in some embodiments.According to embodiments, the machine learning model is trained usingknown images of damaged parcels as well as types of damage, severity ofdamage, cost associated with the damage, and cause of the damage. Themodel is trained in either a supervised or semi-supervised manner. Insome embodiments, however, the model is not trained, such that the modelis unsupervised. Accordingly, every data input can be ingested or fedthrough the model and a corresponding output is generated without regardto monitoring or feedback of the output.

In embodiments, the model can then be called by an interfacingapplication or system and return a prediction according to what data themodel is designed to predict. The predictive output of the machinelearning model can include, for example, an indication of damagedetected from the digital images, a diagnosis and/or characterization ofthe damage, an estimated cost associated with the damage, as well as oneor more possible causes of the damage. The predictive output alsoenables pin-pointing (e.g., via Global Positioning System (GPS)geo-coordinates) where in the transportation and logistics network thedamage is occurring.

According to some embodiments, events are driven based upon thepredictive output of the machine learning model. For example, if a pointin the transportation and logistics network is deemed as the location ofseveral similar types of damage, an automated adjustment can be made toequipment or conditions at that point to avoid or limit future damage toparcels.

Parcels within a transportation and logistics network can traversemultiple locations. At any location within the carrier's logisticnetwork, or between points for that matter, damage of any type may becaused to a parcel. Damage to parcels can be costly and difficult to pinpoint, mitigate, and prevent through the use of tedious and clumsyhuman/visual estimation.

The inventors have determined that resources dedicated to suchassessment and mitigation of parcel damage are easily exhausted due tothe unpredictable complexity of a route traversed by a parcel through acarrier's logistic network. Further, the inventors have determined thattime to mitigation is inexcusably compromised due to human error.

As such, the inventors have determined that the ability to capturemultiple digital images representing the condition of a parcelthroughout a carrier's logistic network and programmatically assess andmitigate any damage as it occurs dramatically increases the efficientuse of computing resources.

FIG. 7 illustrates an exemplary parcel transit route 700 for use withembodiments of the present disclosure. In various embodiments, a parceltransit route 700 may comprise a plurality of parcel interaction points701-707 through which a parcel 710 traverses from origin 701 todestination 707. In the example illustrated in FIG. 7 , an origininteraction point 701 may be a residence from where the parcel 710 isoriginally retrieved by a parcel transit service.

The parcel 710 may interact with a second parcel interaction point 702,which may be a manual delivery vehicle 100 as defined above. The parcel710 may continue through the parcel carrier's logistic network to a nextparcel interaction point 703, which may be inside or at a vehicle 712,such as a hand truck or forklift type assistance device for moving theparcel from the manual delivery vehicle 100 to or within a packagecenter or hub or other parcel storage facility. The vehicle 712, in someembodiments, may alternatively be a conveying mechanism 115 as definedherein.

The parcel 710 may interact with a next parcel interaction point 704,which in some embodiments may be a package center or hub or other parcelstorage facility, such as a sorting facility. Next, the parcel 710 mayinteract with a next parcel interaction point 705, which may be a handtruck or forklift type assistance device for moving the parcel from thepackage center or hub to a manual delivery vehicle 100 and/or within apackage center or hub and/or to a conveying mechanism 115 as definedherein.

Next, the parcel 710 may interact with an autonomous vehicle 140 ormanual delivery vehicle 100 (e.g., as described with reference to FIG. 4) at a next parcel interaction point 706. Finally, in this embodiment,the parcel 710 interacts with a destination interaction point 707, whichmay be a residence or point of business.

Throughout the parcel carrier's logistic network 700 that is traversedby a parcel 710, some or each parcel interaction point 701-707 (and/orareas between the points 701-707) is equipped according to the presentdisclosure with one or more digital image capture mechanisms/systemsand/or other identification capturing mechanism (e.g., theimage/acquisition device 401 as defined herein). As parcel 710 traversesthrough parcel transit route 700, some or each of the interactionpoints, and/or paths along these points, may include a digital imagecapture mechanism/system that captures one or more digital imagesrepresenting one or more fields of view of the parcel 710.

In an illustrative example of image capturing at or along some or eachof these interaction points, in some embodiments, a first digital imagecapture mechanism can be fastened to a worker or driver (e.g., on anarticle of clothing) of the vehicle 100. Accordingly, between the timeat which the driver approaches or picks up the parcel 710 at interactionpoint 701 and when the driver places the parcel 710 in a storagelocation within the vehicle 100 at the second parcel interaction point702, the first digital image capture mechanism may capture images ordetect any potential damage to the parcel 710 that the driver may causevia the handling of the parcel 710. In another example, the storagelocation within the vehicle 100 at the second parcel interaction point702 may additionally or alternatively include a second digital imagecapture mechanism, such that it captures images or detects any damageincurred to the parcel 710 while the vehicle 100 is traveling and whilethe parcel 710 is within a field of view of the second digital imagecapture mechanism. In another example, the first digital image capturemechanism fastened to the driver can capture images or detect damage tothe parcel 710 between a stopping time of the vehicle 100 and a time atwhich the driver arrives to the vehicle 712 within the next parcelinteraction point 703. The vehicle 712 may alternatively or additionallyfurther include a third digital image capture mechanism configured tocapture images or detect damage to the parcel 710 while the user of thevehicle 703 is engaging with the parcel 710 (e.g., lifting the parcel710 via a forklift). In yet another example, a fourth digital imagecapture mechanism may be fasted to the user 716 of the vehicle 712.Accordingly, in some embodiments, the fourth digital image capturemechanism is configured to capture images of the parcel 710 and/ordetect damage between the interaction points 703 and 704. In someembodiments, the interaction point 704 represents a warehouse or otherintermediate facility that includes the environment as described withreference to FIG. 6A. Accordingly, in some embodiments the environmentincludes a fifth digital image capture mechanism (the image capturesystem 400). In some embodiments, the vehicle 720 and/or the user 717alternatively or additionally includes a sixth digital image capturemechanism to detect damage and/or capture images between the picking upof the parcel 710 at the parcel interaction point 704 and the droppingoff of the parcel at the interaction point 706. In some embodiments, theautonomous vehicle 140 is within the interaction point 706 andadditionally or alternatively includes a seventh digital image capturemechanism such that images and/or damage of the parcel 710 can bedetected between the time the autonomous vehicle 140 leaves theinteraction point 706 (e.g., a top of the vehicle 100) and the drop offof the parcel 710 at the interaction point 707.

It will be appreciated that, throughout the parcel carrier's logisticnetwork 700 that is traversed by a parcel 710, each interaction point701-707 may be any one of the types of parcel interaction points asdefined herein. For example, instead of origin interaction point 701being a residence, it may be a place of business. In another example,instead of destination interaction point 707 being a residence, it maybe a place of business. As such, it will be appreciated that multipleintervening parcel interaction points can be present and traversed byparcel 710 within the parcel carrier's logistic network 700. It willalso be appreciated that a parcel carrier's logistic network may havefewer or more interaction points than are depicted in the example inFIG. 7 .

FIG. 6B illustrates an exemplary multi-view image capture system for usewith embodiments of the present disclosure. As will be recognized, andas described above, various types of imaging devices and systems 401 canbe used to capture digital images and other information/data about aparcel 710—including imaging devices and systems associated with manualdelivery vehicles 100, analysis computing entities 105, mobile computingentities 110, one or more autonomous vehicles 140, and/or the like (atvarious points in the transportation and logistics network). The digitalimages may comprise timestamps indicative of the time they werecaptured, location information/data (e.g., geo-coordinates) indicativeof the location they were captured, device/entity information/dataindicative of the device/entity that captured the digital images, and/orthe like. In embodiments, parcel interaction points and/or points alonga carrier's logistic network are equipped with data or digital imagecapturing mechanisms/devices 401A-401N through which one or more of aplurality of fields of view of a parcel 710 can be captured andtransmitted from the parcel interaction point to an analysis computingentity 105 via one or more networks 135.

In embodiments, a parcel 710 may be surrounded by a plurality ofacquisition devices 401A-401N. Each image/acquisition device 401A-401Nhas associated therewith a field of view or pose view 403A-403Nrepresenting various views of the parcel 710. Digital files representingidentifying information/data, including digital images or otherwise(e.g., including parcel identification information as described herein),are transmitted from devices/mechanisms 401A-401N to analysis computingentity 105 via one or more networks 135.

In embodiments, a parcel 710 may be associated with a rotation mechanismsuch that a single image/acquisition device 401 (and/or otherappropriate computing entity) may capture multiple digital imagesrepresenting different fields of view of the parcel 710 (i.e., withoutthe need for multiple acquisition or collection devices). In suchembodiments, a signal acquisition device 401 (and/or other appropriatecomputing entity) may locally store all acquired/collected images and/ordata to be transmitted in a single transmission to an analysis computingentity 105 via one or more networks 135. And as will be recognized,various other entities (such as those described above) can be used tocapture one or more images of parcel 710.

FIG. 8 illustrates an example process 800 for use with embodiments ofthe present disclosure. The process 800 and/or 900 may be performed byprocessing logic that comprises hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (e.g.,instructions run on a processor to perform hardware simulation),firmware, or a combination thereof. In embodiments, multiple views(e.g., from some or each of the image/acquisition devices 401) of aparcel are digitally combined in order to be processed by a machinelearning model. In some embodiments, an analysis computing entity 105according to the present disclosure performs processing using themachine learning model. It will be appreciated that there are a varietyof multi-view learning approaches that may be employed to arrive at amulti-view damage prediction assessment and mitigation result asdescribed herein. The following description is provided for exemplarypurposes only.

In embodiments, each digital image representing one of a plurality offields of view is processed such that each pixel of the digital image isextracted (Operation/Step). The extracted pixels are used to determinewhether any overlap exists between fields of view of each of the digitalimages (Operation/Step 802). If overlaps exist, those pixels associatedwith the overlaps are removed (Operation/Step 803). The resultingdigital information representing fields of view without overlaps, alongwith additional identifying information related to the parcel asdescribed herein, are provided to a machine learning model(Operation/Step 804) at an analysis computing entity 105 via one or morenetworks 135 according to embodiments of the present disclosure.

FIG. 9 illustrates an example process 900 for use with embodiments ofthe present disclosure. According to particular embodiments, an analysiscomputing entity 105 of the present disclosure receives a firstplurality of parcel digital images from an origin interaction point(Operation/Step 901). In some embodiments, the first plurality of parceldigital images is associated with a parcel being transported from theorigin interaction point to the destination interaction point via theplurality of parcel interaction points along a parcel carrier's logisticnetwork. For example, the plurality of parcel digital images can betaken at the origin interaction point and/or from the origin interactionpoint to a next interaction point. An example of a parcel carrier'slogistic network 700 is depicted in FIG. 7 . In embodiments, additionalidentifying information/data related to the parcel is also received bythe analysis computing entity 105.

Process 900 continues with the analysis computing entity 105 of thepresent disclosure receiving a second plurality of parcel digital imagesof the parcel from a first parcel interaction point of the plurality ofparcel interaction points (Operation/Step 902). In some embodiments, thefirst plurality of parcel digital images and the second plurality ofparcel digital images represent a plurality of fields of view of theparcel at different locations along a parcel carrier's logistic network(e.g., some or each parcel interaction point (and/or along such points)of the parcel carrier logistic network 700). In embodiments, additionalidentifying information related to the parcel is also received by theanalysis computing entity 105.

Process 900 continues with the analysis computing entity 105 of thepresent disclosure programmatically generating a first parcel damageanalysis based upon the first plurality of parcel digital images, thesecond plurality of parcel digital images, and a machine learning model(Operation/Step 903). In some embodiments, the first parcel damageanalysis is also based upon any additional identifying informationrelated to the parcel that has been received by the analysis computingentity 105.

The parcel damage analysis can include any suitable machine learning orobject recognition method for detecting and analyzing the damage. Forexample, in some embodiments, the analysis computing entity 105 includesa data store of parcel images of parcels that are damaged and are notdamaged outside of a threshold. Accordingly, when a received image isanalyzed, the image of the parcel may be compared against one or moreimages within the data store. If there is a match (or substantial match)between the received image(s) and the image(s) within the data store,there may be no damage. To the contrary, if the images do not match orare outside of a threshold (e.g., the received image includes acompressed corner of a package and the data store of images does notinclude the compressed corner), transit network interaction point damageanalyses can be generated and transmitted, as described in operations906 and 907. In some embodiments, machine learning models are used tohelp classify whether particular input parcel images correspond todamaged or not damaged parcels, particular types of damage, and/or otherparameters associated with parcel damage as describe herein. In someembodiments, these models are trained using historical digital images ofknown damage parcels and/or images of known non-damaged parcels. In thisway, the system can determine when a parcel is damaged and how it isdamaged based on one or more historical patterns or known objectrecognition damage characteristics of past images.

In an example illustration of how machine learning models can be used toclassify parcel damage or come up with target variables, one or moreneural networks (e.g., convoluted neural networks) can be used. Variouscategories or classifications can first be identified, such as parcelsthat are “damaged” or “not damaged.” Other classification examples mayadditionally or alternatively be damage types, such as “water damage,”“heat damage,” “compression damage,” “tension damage,” “bending damage,”“shear damage.” The neural network can include a convolutional layer, apooling layer, and a fully connected layer. The machine learning modelneural network may be fed or receive as input one or more images ofparcels at the convolutional layer. Each input image can be transformedinto a 2-D input vector array of values, such as integers of ones andzeroes. Each value represents or describes a particular pixel of theimage and the pixel's intensity. For instance, each line or edge of aparcel in the image can be denoted with a one and each non-line can berepresented with zeroes. The convolutional layer utilizes one or morefilter maps, which each represent a feature (e.g., a sub-image) of theinput image (e.g., a corner of a parcel, mid-section of a parcel, top ofparcel, etc.). There may be various features of an image and thus theremay be various linearly stacked filter maps for a given image. A filtermap is also an array of values that represent sets of pixels and weightswhere a value is weighted higher when it matches a corresponding pixelor set of pixels in the corresponding section of the input image. Theconvolution layer includes an algorithm that uses each filter map toscan or analyze each portion of the input image. Accordingly, each pixelof each filter map is compared and matched up against a correspondingpixel in each section of the input image and weighted according tosimilarity. In some embodiments, the convolutional layer performs linearfunctions or operations to arrive at the filter map by multiplying eachimage pixel value with its own value and then performing a summationfunction of each product, which is then divided by the total quantity ofpixels in the image feature.

In particular embodiments, the pooling layer reduces the dimensionalityor compresses each feature map by picking a window size (i.e., aquantity of dimensional pixels that will be analyzed in the feature map)and selecting the maximum value of all of the values in the feature mapas the only output for the modified feature map. In some embodiments,the fully connected layer maps votes for each pixel of each modifiedfeature to each classification (e.g., types of damages, “damaged,” or“not damaged,” etc.). The vote strength of each pixel is based on itsweight or value score. The output is a score (e.g., a floating pointvalue, where 1 is a 100% match) that indicates the probability that agiven input image or set of modified features fits within a particulardefined class (e.g., damaged or not damaged). For example, an inputimage may include a first picture of a parcel that has a large dent. Theclassification types may be “water damage,” “puncture damage,” and “dentdamage.” After the first picture is fed through each of the layers, theoutput may include a floating point value score for each damageclassification type that indicates “water damage: 0.21,” “puncturedamage: 0.70,” and “dent damage: 0.90,” which indicates that the parcelof the parcel image likely has experienced dent damage, given the 90%likelihood. Training or tuning can include minimizing a loss functionbetween the target variable or output (e.g., 0.90) and the expectedoutput (e.g., 100%). Accordingly, it may be desirable to arrive as closeto 100% confidence of a particular classification as possible so as toreduce the prediction error. This may happen overtime as more trainingimages and baseline data sets are fed into the learning models so thatclassification can occur with higher prediction probabilities. In someembodiments, the severity of the damage is additionally classified(e.g., “slight damage,” “moderate damage,” and “heavy damage”) inresponse to detecting or determining damage. In these embodiments, themachine learning model can function according to the steps describedabove. The system also re-trains itself with each processed digitalimage. Accordingly, the more images it processes, the better it gets orthe more accurate the prediction becomes.

If a severity of the first parcel damage analysis satisfies (e.g., isbelow) a threshold (Operation/Step 904), the analysis computing entity105 of the present disclosure transmits a first transit networkinteraction point condition confirmation based upon the first parceldamage analysis (Operation/Step 905). For example, the analysiscomputing entity 105 can transmit, via the network 135, a notificationto computing entity 110 the indicating that there is no damage to theparcel and accordingly, the travelling or traversing of the parcel maycontinue down the transit network.

If the severity of the first parcel damage analysis fails to satisfy(e.g., is above) the threshold (Operation/Step 904), the analysiscomputing entity 105 of the present disclosure programmaticallygenerates a first transit network interaction point damage analysisbased upon the first parcel damage analysis and the machine learningmodel (Operation/Step 906). In embodiments, the analysis computingentity 105 of the present disclosure then transmits a first transitnetwork interaction point damage mitigation instruction (e.g., to themobile computing entity 110) based upon the first transit networkinteraction point damage analysis (Operation/Step 907). The mitigationinstruction can be also be based on the time, location, and/ordevice/entity information/data in the digital images. In someembodiments, analysis computing entity 105 transmits a transit networkinteraction point damage mitigation instruction comprising a controlsignal to automatically stop, slow, modify, or alter a conveyingmechanism 115or any other device. In some embodiments, analysiscomputing entity 105 transmits a transit network interaction pointdamage mitigation instruction comprising a control signal to one or moredevices in order to automatically adjust environmental controls (e.g.,temperature, humidity, water controls, opening/closing of windows ordoors) within a manual delivery vehicle 100, autonomous vehicle 140,package center or hub or other parcel storage facility, and the like.

In some embodiments, a signal (e.g., a notification and/or a controlsignal) may be provided to any suitable computing device based at leaston the determining of the likelihood associated with damage of one ormore parcels. The providing of the signal may modify a computing deviceor a condition (e.g., adjust temperature, change air conditioning,open/close door etc.), such as described above. For example, themodifying may include causing (e.g., by the analysis computing entity105) a computing device (e.g., the mobile computing entity 110) todisplay a notification indicating damage analysis and/or damage analysissummary. In another example, the modifying may be or include causing oneor more computing devices (e.g., via a control signal) to modify one ormore environmental conditions, such as causing an autonomous vehicleapparatus to slow down or stop. The providing of the signal inparticular embodiments includes the transit network interaction pointcondition confirmation and/or a mitigation instruction, as describedherein, which can modify a computing device by causing the computingdevice to display the mitigation instruction and/or transit networkinteraction point condition confirmation.

In some embodiments, analysis computing entity 105 transmits a transitnetwork interaction point damage mitigation instruction comprising arepackage and/or rewrap instruction to a mobile computing entity 110operated by a user. In such embodiments, a display is rendered on themobile computing entity 110 providing a notification to the user that aparticular package is to be repackaged or rewrapped due to damage to itsexterior.

In some embodiments, analysis computing entity 105 transmits a transitnetwork interaction point damage mitigation instruction comprising anotification to one or more computing entities operated by a user, acustomer (e.g., shipper or receiver), and the like. In such embodiments,the notification renders on a display of the corresponding computingentity providing an indication of damage to a parcel and/or mitigationmeasures taking place as a result of the known damage.

In some embodiments, a transit network interaction point damagemitigation instruction can comprise signals to multiple entitiesthroughout a carrier's logistic network. For example, a transit networkinteraction point damage mitigation instruction may comprise a controlsignal to automatically stop, slow, or alter/modify a conveyingmechanism 115. Such a transit network interaction point damagemitigation instruction may also provide for re-routing of packagesalready in contact with or scheduled to have contact with the conveyingmechanism. Such a transit network interaction point damage mitigationinstruction may also provide for notifying one or more mobile computingentities 110 that a conveying mechanism has been slowed/stopped/alteredand that packages have been re-routed as a result. Such a transitnetwork interaction point damage mitigation instruction may also providefor notifying a customer of any potential delay in delivery of parcelsimpacted by the instruction.

In some embodiments, analysis computing entity 105 transmits a transitnetwork interaction point damage mitigation instruction comprising acontrol signal to automatically stop, slow, or alter an autonomousvehicle 140 (and/or vehicle 100). In additional embodiments, such atransit network interaction point damage mitigation instruction may alsoprovide for re-routing of packages already in contact with or scheduledto have contact with the autonomous vehicle 140. Such a transit networkinteraction point damage mitigation instruction may also provide fornotifying one or more mobile computing entities 110 that an autonomousvehicle 140 has been slowed/stopped/altered and that packages have beenre-routed as a result. Such a transit network interaction point damagemitigation instruction may also provide for notifying a customer (e.g.,via auditory instruction) or customer's computing device of (e.g., via adisplayed notification) any potential delay in delivery of parcelsimpacted by the instruction.

In some embodiments, analysis computing entity 105 transmits a transitnetwork interaction point damage mitigation instruction comprising acontrol signal to automatically schedule maintenance to a manualdelivery vehicle 100. In such an embodiment, a maintenance provider mayautomatically be dispatched to the manual delivery vehicle 100 based onGPS coordinates associated with the manual delivery vehicle 100. Inadditional embodiments, such a transit network interaction point damagemitigation instruction may also provide for re-routing of packagesalready in contact with or scheduled to have contact with the manualdelivery vehicle 100. Such a transit network interaction point damagemitigation instruction may also provide for notifying one or more mobilecomputing entities 110 that manual delivery vehicle 100 has beenscheduled for maintenance and that packages have been re-routed as aresult. Such a transit network interaction point damage mitigationinstruction may also provide for notifying a customer of any potentialdelay in delivery of parcels impacted by the instruction.

In embodiments of the present disclosure, the analysis computing entity105 receives (e.g., from the camera 326) identifying informationassociated with a parcel in addition to digital images representing theparcel. In embodiments, other information associated with an interactionpoint may be received or determined by the analysis computing entity105. Such information may include metadata, such as temperature at thetime the image was taken, time of day the image was taken, typicalambient conditions at the time the image was taken, historical damagerisk, and the like. In some embodiments, this identifying informationhelps generate the first parcel damage analysis and/or helps generatemitigation instructions. For example, if the ambient temperature is over115 degrees Fahrenheit combined with loosely fitting or detachedpackaging tape as identified by an imaging capturing device, aninference may be made based on both of these two observations that heathas caused the package to become unstable. Accordingly, a mitigationinstruction can be sent from the computing entity 105 to the mobilecomputing entity 110 indicating that new tape should be used to re-rapthe package, as well as a mitigation instruction that causes a vehicleto lower its air conditioner to a cooler temperature.

In embodiments of the present disclosure, all information related todamage analyses and condition confirmations is logged by the analysiscomputing entity 105 and stored in one or more associated non-volatilestorage devices 210 (e.g., databases or data stores as described herein)and/or volatile storage devices.

In embodiments of the present disclosure, notifications may be providedbased upon any determination or status to a shipper, a receiver, and/orinternally to a parcel transit provider.

According to embodiments, the present system receives digital images ofparcels at various points throughout a transportation and logisticsnetwork. Particular embodiments of the present disclosure detect,characterize, diagnose, and root-cause any damage based upon a trainedmachine learning model. In embodiments, the machine learning model is aconvolutional neural network.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

The methods, apparatus, and computer program products described hereinare further operable to receive a second plurality of parcel digitalimages of the parcel from a first parcel interaction point of theplurality of parcel interaction points, the first plurality of parceldigital images and the second plurality of parcel digital imagesrepresenting a plurality of fields of view of the parcel.

The methods, apparatus and computer program products described hereinare further operable to programmatically generate a first parcel damageanalysis based upon the first plurality of parcel digital images, thesecond plurality of parcel digital images, and a machine learning model.

The methods, apparatus and computer program products described hereinare further operable to, upon determining that a severity of the firstparcel damage analysis is below a threshold, transmit a first transitnetwork interaction point condition confirmation based upon the firstparcel damage analysis.

The methods, apparatus and computer program products described hereinare further operable to, upon determining that the severity of the firstparcel damage analysis is above the threshold, programmatically generatea first transit network interaction point damage analysis based upon thefirst parcel damage analysis and the machine learning model, andtransmit a first transit network interaction point damage mitigationinstruction based upon the first transit network interaction pointdamage analysis.

Optionally, in embodiments of the present disclosure, the first parceldamage analysis comprises determining a first plurality of pose rangesfor the first plurality of parcel digital images.

Optionally, in embodiments of the present disclosure, the first parceldamage analysis further comprises determining a second plurality of poseranges for the second plurality of parcel digital images.

Optionally, in embodiments of the present disclosure, the first parceldamage analysis further comprises determining a first plurality ofparcel view overlaps based upon the first plurality of pose ranges anddetermining a second plurality of parcel view overlaps based upon thesecond plurality of pose ranges.

Optionally, in embodiments of the present disclosure, the first parceldamage analysis further comprises programmatically generating the firstparcel damage analysis based upon the first plurality of parcel viewoverlaps, the second plurality of parcel view overlaps, and the machinelearning model.

Optionally, in embodiments of the present disclosure, the first transitnetwork interaction point damage analysis comprises a first transitnetwork interaction point identifier, a parcel identifier, and a firstparcel damage analysis summary.

Optionally, in embodiments of the present disclosure, the parcel damageanalysis summary comprises one or more of a parcel type, a parcel damagetype, a parcel damage location identifier, a parcel damage severity, aparcel damage mitigation recommendation, and a parcel damage restorationestimate.

Optionally, in embodiments of the present disclosure, the first transitnetwork interaction point damage mitigation instruction comprises one ormore electronic signals for modifying one or more conditions at atransit network interaction point based upon the parcel damagemitigation recommendation.

The methods, apparatus and computer program products described hereinare further operable to receive a third plurality of parcel digitalimages of the parcel from a second parcel interaction point of theplurality of parcel interaction points, the third plurality of parceldigital images representing the plurality of fields of view of theparcel.

The methods, apparatus and computer program products described hereinare further operable to programmatically generate a second parcel damageanalysis based upon the first plurality of parcel digital images, thesecond plurality of parcel digital images, the third plurality of parceldigital images, and the machine learning model.

The methods, apparatus and computer program products described hereinare further operable to, upon determining that a second severity of thesecond parcel damage analysis is below a second threshold, transmit asecond transit network interaction point condition confirmation basedupon the second parcel damage analysis.

The methods, apparatus and computer program products described hereinare further operable to, upon determining that the second severity ofthe second parcel damage analysis is above the second threshold,programmatically generate a second transit network interaction pointdamage analysis based upon the second parcel damage analysis and themachine learning model and transmit a second transit network interactionpoint damage mitigation instruction based upon the second transitnetwork interaction point damage analysis.

Optionally, in embodiments of the present disclosure, the second parceldamage analysis comprises determining a third plurality of pose rangesfor the third plurality of parcel digital images.

Optionally, in embodiments of the present disclosure, the second parceldamage analysis further comprises determining a third plurality ofparcel view overlaps based upon the third plurality of pose ranges.

Optionally, in embodiments of the present disclosure, the second parceldamage analysis further comprises programmatically generating the secondparcel damage analysis based upon the second plurality of parcel viewoverlaps, the third plurality of parcel view overlaps, and the machinelearning model.

Optionally, in embodiments of the present disclosure, the second transitnetwork interaction point damage analysis comprises a second transitnetwork interaction point identifier, a parcel identifier and a secondparcel damage analysis summary.

Optionally, in embodiments of the present disclosure, the second parceldamage analysis summary comprises one or more of a parcel type, a parceldamage type, a parcel damage location identifier, a parcel damageseverity, a parcel damage mitigation recommendation, and a parcel damagerestoration estimate.

Optionally, in embodiments of the present disclosure, the second transitnetwork interaction point damage mitigation instruction comprises one ormore electronic signals for modifying one or more conditions at atransit network interaction point based upon the parcel damagemitigation recommendation.

Optionally, in embodiments of the present disclosure determining alikelihood associated with a damage of the first parcel includesdetermining a likelihood includes: identifying a set of outputclassification categories that specify whether a given parcel is damagedor not damaged outside of a threshold, receiving a historical set ofdigital images, feeding the historical set of digital images through amachine learning model, outputting, via the machine learning model, eachof the historical set of digital images into one of the set of outputclassifications based on scoring the historical set of digital images,tuning (e.g., training) the machine learning model based on theoutputting, and in response to feeding the first parcel digital imagethrough the machine learning model, outputting the first parcel digitalimage into one of the set of output classifications based on the tuningof the machine learning model. Some or each of these steps are describedin more detail with reference to FIG. 7 .

Optionally, in some embodiments of the present disclosure upondetermining that a severity of a first parcel damage analysis is above athreshold, a first transit network interaction point damage analysis canbe generated based upon the first parcel damage analysis and a machinelearning model. In response to determining that the severity of thefirst parcel damage analysis being above the threshold, a transitnetwork interaction point damage mitigation instruction can be provided.The transit network interaction point damage mitigation instruction mayinclude providing an instruction to a device within a carrier route thatincludes the first interaction point and the second interaction point.The mitigation instruction may include a control signal to modify acondition to mitigate the damage. These operations are further describedwith reference to “parcel damage mitigation,” FIG. 9 , under the“exemplary system operation” heading contained herein, and various otherparagraphs.

Optionally, in some embodiments of the present disclosure, themodification of a computing device or condition includes adjusting oneor more environmental controls within a manual delivery vehicle, anautonomous vehicle, or a parcel storage facility, as described withreference to at least to “parcel damage mitigation,” FIG. 9 , under the“exemplary system operation” heading contained herein, and various otherparagraphs.

Optionally, in some embodiments of the present disclosure, a providingof a signal to a second computing device includes causing the secondcomputing device to display a notification that indicates how tomitigate the damage, as described with reference to at least the“transit network interaction point damage mitigation instruction,”operation 905 of FIG. 9 , or any discussion of FIG. 9 .

V. Conclusion

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing description and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation, unlessdescribed otherwise.

What is claimed is:
 1. A system comprising: at least one first computingdevice having at least one processor; and at least one computer readablestorage medium having program instructions embodied therewith, theprogram instructions readable or executable by the at least oneprocessor to cause the system to: receive a first parcel digital imagecaptured from a first parcel interaction point, the first parcel digitalimage includes a first representation of a first parcel; providing, asinput, the first parcel digital image to one or more machine learningmodels; in response to the providing, converting one or more portions ofthe first parcel digital image into a vector of values, the vector ofvalues representing the one or more portions of the first parcel digitalimage; in response to the converting and based at least in part ontraining the one or more machine learning models, determining alikelihood associated with damage of the first parcel.
 2. The system ofclaim 1, wherein the determining of the likelihood associated withdamage of the first parcel includes classifying a damage type that thefirst parcel has experienced.
 3. The system of claim 1, wherein thedetermining of the likelihood associated with damage of the first parcelincludes classifying whether the first parcel has been damaged or notdamaged.
 4. The system of claim 1, wherein the first parcel includes alikelihood of damage above a threshold, and wherein the at least oneprocessor further causes the system to cause the system to cause displayof a notification that indicates how to mitigate the damage.
 5. Thesystem of claim 1, wherein the determining of the likelihood associatedwith damage of the first parcel includes classifying a severity of thedamage.
 6. The system of claim 1, wherein the determining of thelikelihood associated with damage of the first parcel includes at leastone of: generating a parcel damage restoration estimate, determining alocation that the parcel was damaged at, determining one or more causesof the damage, and generating a parcel damage mitigation recommendation.7. The system of claim 1, wherein the training of the one or moremachine learning models is based on using known images of damagedparcels and known images of non-damaged parcels, and wherein thedetermining of the likelihood is based on learning patterns of the knownimages of damaged parcels.
 8. The system of claim 1, wherein the atleast one processor further causes the system to: receive a secondparcel digital image captured from a second parcel interaction point,the second parcel digital image includes a second representation of thefirst parcel; providing, as input, the second parcel digital image tothe one or more machine learning models; in response to the providing ofthe second parcel to the one or more machine learning models, convertingone or more portions of the second parcel digital image into a secondvector of values; based at least in part on the converting of the one ormore portions of the second parcel digital image into the second vectorof values, combining the first parcel digital image with the secondparcel digital image, wherein the determining of the likelihoodassociated with damage of the first parcel is based at least in part onthe combining.
 9. A computer-implemented method comprising: training oneor more machine learning models based on using a first set of images ofdamaged parcels and a second set of images of non-damaged parcels;subsequent to the training, receiving a first parcel digital image, thefirst parcel digital image includes a first representation of a firstparcel; providing, as input, the first parcel digital image to the oneor more machine learning models; converting, via the one or more machinelearning models, one or more portions of the first parcel digital imageinto a vector of values, the vector of values representing the one ormore portions of the first parcel digital image; based at least in parton the training the one or more machine learning models, determining alikelihood associated with damage of the first parcel.
 10. Thecomputer-implemented method of claim 9, wherein the determining of thelikelihood associated with damage of the first parcel includesclassifying a damage type that the first parcel has experienced.
 11. Thecomputer-implemented method of claim 9, wherein the determining of thelikelihood associated with damage of the first parcel includesclassifying whether the first parcel has been damaged or not damaged.12. The computer-implemented method of claim 9, wherein the first parcelincludes a likelihood of damage above a threshold, and wherein the atleast one processor further causes the system to cause the system tocause display of a notification that indicates how to mitigate thedamage.
 13. The computer-implemented method of claim 9, wherein thedetermining of the likelihood associated with damage of the first parcelincludes classifying a severity of the damage.
 14. Thecomputer-implemented method of claim 9, wherein the determining of thelikelihood associated with damage of the first parcel includes at leastone of: generating a parcel damage restoration estimate, determining alocation that the parcel was damaged at, determining one or more causesof the damage, and generating a parcel damage mitigation recommendation.15. An apparatus for predictive parcel damage mitigation in a parceltransit network, the apparatus comprising at least one processor and atleast one memory including computer program code, the at least onememory and the computer program code configured to, with the at leastone processor, cause the apparatus to: train one or more machinelearning models based at least in part on using a first set of images ofdamaged parcels; subsequent to the training, receive a first parceldigital image captured from a first parcel interaction point, the firstparcel digital image includes a first representation of a first parcel;and based at least in part on the training the one or more machinelearning models, determine, via the one or more machine learning models,a likelihood associated with damage of the first parcel.
 16. Theapparatus of claim 15, wherein the determining of the likelihoodassociated with damage of the first parcel includes classifying a damagetype that the first parcel has experienced.
 17. The apparatus of claim15, wherein the determining of the likelihood associated with damage ofthe first parcel includes classifying whether the first parcel has beendamaged or not damaged.
 18. The apparatus of claim 15, wherein the firstparcel includes a likelihood of damage above a threshold, and whereinthe at least one processor further causes the apparatus to cause thesystem to cause display of a notification that indicates how to mitigatethe damage.
 19. The apparatus of claim 15, wherein the determining ofthe likelihood associated with damage of the first parcel includesclassifying a severity of the damage.
 20. The apparatus of claim 15,wherein the determining of the likelihood associated with damage of thefirst parcel includes at least one of: generating a parcel damagerestoration estimate, determining a location that the parcel was damagedat, determining one or more causes of the damage, and generating aparcel damage mitigation recommendation.